[
{
"key": "YZDINHUI",
"version": 5850,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/YZDINHUI",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/YZDINHUI",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/7UK9DXAL",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "YZDINHUI",
"version": 5850,
"parentItem": "7UK9DXAL",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Stevens et al_2023_BioCLIP.pdf",
"accessDate": "2024-03-22T21:09:47Z",
"url": "https://arxiv.org/pdf/2311.18803.pdf",
"note": "
Contents
",
"contentType": "application/pdf",
"charset": "",
"filename": "Stevens et al_2023_BioCLIP.pdf",
"md5": null,
"mtime": null,
"tags": [],
"relations": {},
"dateAdded": "2024-03-22T21:09:47Z",
"dateModified": "2024-03-22T21:09:48Z"
}
},
{
"key": "T69Q7D45",
"version": 5851,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/T69Q7D45",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/T69Q7D45",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/7UK9DXAL",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
}
},
"data": {
"key": "T69Q7D45",
"version": 5851,
"parentItem": "7UK9DXAL",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "arXiv.org Snapshot",
"accessDate": "2024-03-22T21:09:40Z",
"url": "https://arxiv.org/abs/2311.18803",
"note": "",
"contentType": "text/html",
"charset": "utf-8",
"filename": "2311.html",
"md5": "d19a16ce9453832b69831abc0a8376d3",
"mtime": 1711141780000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-22T21:09:40Z",
"dateModified": "2024-03-22T21:09:40Z"
}
},
{
"key": "NYYWBM9I",
"version": 5849,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/NYYWBM9I",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/NYYWBM9I",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/7UK9DXAL",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "NYYWBM9I",
"version": 5849,
"parentItem": "7UK9DXAL",
"itemType": "note",
"note": "Comment: 18 pages; updated title",
"tags": [],
"relations": {},
"dateAdded": "2024-03-22T21:09:34Z",
"dateModified": "2024-03-22T21:09:34Z"
}
},
{
"key": "7UK9DXAL",
"version": 5849,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/7UK9DXAL",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/7UK9DXAL",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"creatorSummary": "Stevens et al.",
"parsedDate": "2023-12-04",
"numChildren": 3
},
"data": {
"key": "7UK9DXAL",
"version": 5849,
"itemType": "preprint",
"title": "BioCLIP: A Vision Foundation Model for the Tree of Life",
"creators": [
{
"creatorType": "author",
"firstName": "Samuel",
"lastName": "Stevens"
},
{
"creatorType": "author",
"firstName": "Jiaman",
"lastName": "Wu"
},
{
"creatorType": "author",
"firstName": "Matthew J.",
"lastName": "Thompson"
},
{
"creatorType": "author",
"firstName": "Elizabeth G.",
"lastName": "Campolongo"
},
{
"creatorType": "author",
"firstName": "Chan Hee",
"lastName": "Song"
},
{
"creatorType": "author",
"firstName": "David Edward",
"lastName": "Carlyn"
},
{
"creatorType": "author",
"firstName": "Li",
"lastName": "Dong"
},
{
"creatorType": "author",
"firstName": "Wasila M.",
"lastName": "Dahdul"
},
{
"creatorType": "author",
"firstName": "Charles",
"lastName": "Stewart"
},
{
"creatorType": "author",
"firstName": "Tanya",
"lastName": "Berger-Wolf"
},
{
"creatorType": "author",
"firstName": "Wei-Lun",
"lastName": "Chao"
},
{
"creatorType": "author",
"firstName": "Yu",
"lastName": "Su"
}
],
"abstractNote": "Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks, and find that BioCLIP consistently and substantially outperforms existing baselines (by 17% to 20% absolute). Intrinsic evaluation reveals that BioCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability. Our code, models and data will be made available at https://github.com/Imageomics/bioclip.",
"genre": "",
"repository": "arXiv",
"archiveID": "arXiv:2311.18803",
"place": "",
"date": "2023-12-04",
"series": "",
"seriesNumber": "",
"DOI": "",
"citationKey": "",
"url": "http://arxiv.org/abs/2311.18803",
"accessDate": "2024-03-22T21:09:34Z",
"archive": "",
"archiveLocation": "",
"shortTitle": "BioCLIP",
"language": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv:2311.18803 [cs]",
"tags": [
{
"tag": "Computer Science - Computation and Language",
"type": 1
},
{
"tag": "Computer Science - Computer Vision and Pattern Recognition",
"type": 1
},
{
"tag": "Computer Science - Machine Learning",
"type": 1
}
],
"collections": [
"9UAEYQNU"
],
"relations": {},
"dateAdded": "2024-03-22T21:09:34Z",
"dateModified": "2024-03-22T21:09:34Z"
}
},
{
"key": "IYATWHYP",
"version": 5848,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/IYATWHYP",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/IYATWHYP",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/6QMKN5NX",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "IYATWHYP",
"version": 5848,
"parentItem": "6QMKN5NX",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Bussière_Potgieter_2023_KAZA Elephant Survey 2022.pdf",
"accessDate": "2024-03-18T02:26:56Z",
"url": "https://www.researchgate.net/profile/Elsa-Bussiere/publication/373555995_KAZA_Elephant_Survey_2022_Volume1_Results_and_Technical_Report/links/64f1ab12c40f1d22df82e6d2/KAZA-Elephant-Survey-2022-Volume1-Results-and-Technical-Report.pdf",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Bussière_Potgieter_2023_KAZA Elephant Survey 2022.pdf",
"md5": "28686f2d7d238ef2e6203dc20298cd9f",
"mtime": 1710728816000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-18T02:26:56Z",
"dateModified": "2024-03-18T02:26:57Z"
}
},
{
"key": "6QMKN5NX",
"version": 5845,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/6QMKN5NX",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/6QMKN5NX",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"creatorSummary": "Bussière and Potgieter",
"parsedDate": "2023",
"numChildren": 1
},
"data": {
"key": "6QMKN5NX",
"version": 5845,
"itemType": "journalArticle",
"title": "KAZA Elephant Survey 2022",
"creators": [
{
"creatorType": "author",
"firstName": "E. M. S.",
"lastName": "Bussière"
},
{
"creatorType": "author",
"firstName": "D.",
"lastName": "Potgieter"
}
],
"abstractNote": "",
"publicationTitle": "Volume I: Results",
"volume": "",
"issue": "",
"pages": "",
"date": "2023",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "https://www.researchgate.net/profile/Elsa-Bussiere/publication/373555995_KAZA_Elephant_Survey_2022_Volume1_Results_and_Technical_Report/links/64f1ab12c40f1d22df82e6d2/KAZA-Elephant-Survey-2022-Volume1-Results-and-Technical-Report.pdf",
"accessDate": "2024-03-18T02:26:51Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Google Scholar",
"callNumber": "",
"rights": "",
"extra": "",
"tags": [],
"collections": [
"9UAEYQNU"
],
"relations": {},
"dateAdded": "2024-03-18T02:26:51Z",
"dateModified": "2024-03-18T02:26:52Z"
}
},
{
"key": "7HY9ZUE2",
"version": 5844,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/7HY9ZUE2",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/7HY9ZUE2",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 185613,
"username": "simbamangu",
"name": "Howard L Frederick",
"links": {
"alternate": {
"href": "https://www.zotero.org/simbamangu",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "7HY9ZUE2",
"version": 5844,
"itemType": "attachment",
"linkMode": "imported_file",
"title": "KES - Manual and Standards.pdf",
"accessDate": "",
"url": "",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "KES - Manual and Standards.pdf",
"md5": "32d8176ee64130d4f8c9816e89d7e48c",
"mtime": 1710728699000,
"tags": [],
"collections": [
"C94LU9NI",
"F3ISSKJ8"
],
"relations": {},
"dateAdded": "2024-03-18T02:24:59Z",
"dateModified": "2024-03-18T02:24:59Z"
}
},
{
"key": "TWXJD3S9",
"version": 5836,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/TWXJD3S9",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/TWXJD3S9",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/UFCC3FBJ",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "TWXJD3S9",
"version": 5836,
"parentItem": "UFCC3FBJ",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Submitted Version",
"accessDate": "2024-03-04T03:50:24Z",
"url": "https://arxiv.org/pdf/1806.11368",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Kellenberger et al. - 2018 - Detecting mammals in UAV images Best practices to address a substantially imbalanced dataset with d.pdf",
"md5": "f4c8540322073d37315265a3a0e8f7c3",
"mtime": 1709524224000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:50:24Z",
"dateModified": "2024-03-04T03:50:24Z"
}
},
{
"key": "FPKUZPXL",
"version": 5837,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/FPKUZPXL",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/FPKUZPXL",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/UFCC3FBJ",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
}
},
"data": {
"key": "FPKUZPXL",
"version": 5837,
"parentItem": "UFCC3FBJ",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "ScienceDirect Snapshot",
"accessDate": "2024-03-04T03:50:19Z",
"url": "https://www.sciencedirect.com/science/article/abs/pii/S0034425718303067?via%3Dihub",
"note": "",
"contentType": "text/html",
"charset": "utf-8",
"filename": "S0034425718303067.html",
"md5": "3f3138c02ddd1884f7516f7832df3cef",
"mtime": 1709524219000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:50:19Z",
"dateModified": "2024-03-04T03:50:19Z"
}
},
{
"key": "UFCC3FBJ",
"version": 5833,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/UFCC3FBJ",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/UFCC3FBJ",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Kellenberger et al.",
"parsedDate": "2018-10-01",
"numChildren": 2
},
"data": {
"key": "UFCC3FBJ",
"version": 5833,
"itemType": "journalArticle",
"title": "Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning",
"creators": [
{
"creatorType": "author",
"firstName": "Benjamin",
"lastName": "Kellenberger"
},
{
"creatorType": "author",
"firstName": "Diego",
"lastName": "Marcos"
},
{
"creatorType": "author",
"firstName": "Devis",
"lastName": "Tuia"
}
],
"abstractNote": "Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large datasets from computer vision. However, the majority of works related to wildlife focuses only on small datasets (typically subsets of UAV campaigns), which might be detrimental when presented with the sheer scale of real study areas for large mammal census. Methods may yield thousands of false alarms in such cases. In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset. We further introduce novel evaluation protocols that are tailored to censuses and model suitability for subsequent human verification of detections. Using our recommendations, we are able to train a CNN reducing the number of false positives by an order of magnitude compared to previous state-of-the-art. Setting the requirements at 90% recall, our CNN allows to reduce the amount of data required for manual verification by three times, thus making it possible for rangers to screen all the data acquired efficiently and to detect almost all animals in the reserve automatically.",
"publicationTitle": "Remote Sensing of Environment",
"volume": "216",
"issue": "",
"pages": "139-153",
"date": "2018-10-01",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "Remote Sensing of Environment",
"language": "",
"DOI": "10.1016/j.rse.2018.06.028",
"ISSN": "0034-4257",
"shortTitle": "Detecting mammals in UAV images",
"url": "https://www.sciencedirect.com/science/article/pii/S0034425718303067",
"accessDate": "2024-03-04T03:50:13Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "ScienceDirect",
"callNumber": "",
"rights": "",
"extra": "",
"tags": [
{
"tag": "Animal census",
"type": 1
},
{
"tag": "Convolutional Neural Networks",
"type": 1
},
{
"tag": "Deep learning",
"type": 1
},
{
"tag": "Object detection",
"type": 1
},
{
"tag": "Unmanned Aerial Vehicles",
"type": 1
},
{
"tag": "Wildlife monitoring",
"type": 1
}
],
"collections": [
"MU4FW7V5"
],
"relations": {},
"dateAdded": "2024-03-04T03:50:13Z",
"dateModified": "2024-03-04T03:50:13Z"
}
},
{
"key": "TRY37SKB",
"version": 5831,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/TRY37SKB",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/TRY37SKB",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/4W7MVNZ2",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
}
},
"data": {
"key": "TRY37SKB",
"version": 5831,
"parentItem": "4W7MVNZ2",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Snapshot",
"accessDate": "2024-03-04T03:41:49Z",
"url": "https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13581",
"note": "",
"contentType": "text/html",
"charset": "utf-8",
"filename": "2041-210X.html",
"md5": "5509029dbdac14641639e59ef4b73ff8",
"mtime": 1709523709000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:41:49Z",
"dateModified": "2024-03-04T03:41:49Z"
}
},
{
"key": "V8QR5ZAD",
"version": 5830,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/V8QR5ZAD",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/V8QR5ZAD",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/4W7MVNZ2",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "V8QR5ZAD",
"version": 5830,
"parentItem": "4W7MVNZ2",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Full Text PDF",
"accessDate": "2024-03-04T03:41:43Z",
"url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13581",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Corcoran et al. - 2021 - Automated detection of wildlife using drones Synthesis, opportunities and constraints.pdf",
"md5": "9028ab86f63707a42cc3d2852c13f2b3",
"mtime": 1709523703000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:41:43Z",
"dateModified": "2024-03-04T03:41:43Z"
}
},
{
"key": "4W7MVNZ2",
"version": 5828,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/4W7MVNZ2",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/4W7MVNZ2",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Corcoran et al.",
"parsedDate": "2021",
"numChildren": 2
},
"data": {
"key": "4W7MVNZ2",
"version": 5828,
"itemType": "journalArticle",
"title": "Automated detection of wildlife using drones: Synthesis, opportunities and constraints",
"creators": [
{
"creatorType": "author",
"firstName": "Evangeline",
"lastName": "Corcoran"
},
{
"creatorType": "author",
"firstName": "Megan",
"lastName": "Winsen"
},
{
"creatorType": "author",
"firstName": "Ashlee",
"lastName": "Sudholz"
},
{
"creatorType": "author",
"firstName": "Grant",
"lastName": "Hamilton"
}
],
"abstractNote": "Accurate detection of individual animals is integral to the management of vulnerable wildlife species, but often difficult and costly to achieve for species that occur over wide or inaccessible areas or engage in cryptic behaviours. There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms. While manual evaluation of drone-acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers. Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194). We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone-acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods. From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in drone-acquired imagery. A high probability of automated detection could be achieved efficiently using fixed-wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats. The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.",
"publicationTitle": "Methods in Ecology and Evolution",
"volume": "12",
"issue": "6",
"pages": "1103-1114",
"date": "2021",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "10.1111/2041-210X.13581",
"ISSN": "2041-210X",
"shortTitle": "Automated detection of wildlife using drones",
"url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13581",
"accessDate": "2024-03-04T03:41:42Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Wiley Online Library",
"callNumber": "",
"rights": "© 2021 British Ecological Society",
"extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13581",
"tags": [
{
"tag": "UAVs",
"type": 1
},
{
"tag": "drones",
"type": 1
},
{
"tag": "machine learning",
"type": 1
},
{
"tag": "remote sensing",
"type": 1
},
{
"tag": "thermal imaging",
"type": 1
},
{
"tag": "unmanned aerial vehicles",
"type": 1
},
{
"tag": "wildlife detection",
"type": 1
}
],
"collections": [
"MU4FW7V5"
],
"relations": {},
"dateAdded": "2024-03-04T03:41:42Z",
"dateModified": "2024-03-04T03:41:42Z"
}
},
{
"key": "BSG3GCFC",
"version": 5825,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/BSG3GCFC",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/BSG3GCFC",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/W7FDH38M",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
}
},
"data": {
"key": "BSG3GCFC",
"version": 5825,
"parentItem": "W7FDH38M",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Snapshot",
"accessDate": "2024-03-04T03:40:34Z",
"url": "https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13165",
"note": "",
"contentType": "text/html",
"charset": "utf-8",
"filename": "2041-210X.html",
"md5": "c6c06db43dc0d6fc2d058e40cfec5851",
"mtime": 1709523634000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:40:34Z",
"dateModified": "2024-03-04T03:40:34Z"
}
},
{
"key": "PXTZEPAT",
"version": 5826,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/PXTZEPAT",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/PXTZEPAT",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/W7FDH38M",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "PXTZEPAT",
"version": 5826,
"parentItem": "W7FDH38M",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Full Text PDF",
"accessDate": "2024-03-04T03:40:28Z",
"url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13165",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Torney et al. - 2019 - A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey.pdf",
"md5": "17249ff4f463f2662a2873e71aeeb39f",
"mtime": 1709523628000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:40:28Z",
"dateModified": "2024-03-04T03:40:28Z"
}
},
{
"key": "W7FDH38M",
"version": 5823,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/W7FDH38M",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/W7FDH38M",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Torney et al.",
"parsedDate": "2019",
"numChildren": 2
},
"data": {
"key": "W7FDH38M",
"version": 5823,
"itemType": "journalArticle",
"title": "A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images",
"creators": [
{
"creatorType": "author",
"firstName": "Colin J.",
"lastName": "Torney"
},
{
"creatorType": "author",
"firstName": "David J.",
"lastName": "Lloyd-Jones"
},
{
"creatorType": "author",
"firstName": "Mark",
"lastName": "Chevallier"
},
{
"creatorType": "author",
"firstName": "David C.",
"lastName": "Moyer"
},
{
"creatorType": "author",
"firstName": "Honori T.",
"lastName": "Maliti"
},
{
"creatorType": "author",
"firstName": "Machoke",
"lastName": "Mwita"
},
{
"creatorType": "author",
"firstName": "Edward M.",
"lastName": "Kohi"
},
{
"creatorType": "author",
"firstName": "Grant C.",
"lastName": "Hopcraft"
}
],
"abstractNote": "Fast and accurate estimates of wildlife abundance are an essential component of efforts to conserve ecosystems in the face of rapid environmental change. A widely used method for estimating species abundance involves flying aerial transects, taking photographs, counting animals within the images and then inferring total population size based on a statistical estimate of species density in the region. The intermediate task of manually counting the aerial images is highly labour intensive and is often the limiting step in making a population estimate. Here, we assess the use of two novel approaches to perform this task by deploying both citizen scientists and deep learning to count aerial images of the 2015 survey of wildebeest (Connochaetes taurinus) in Serengeti National Park, Tanzania. Through the use of the online platform Zooniverse, we collected multiple non-expert counts by citizen scientists and used three different aggregation methods to obtain a single count for the survey images. We also counted the images by developing a bespoke deep learning method via the use of a convolutional neural network. The results of both approaches were then compared. After filtering of the citizen science counts, both approaches provided highly accurate total estimates. The deep learning method was far faster and appears to be a more reliable and predictable approach; however, we note that citizen science volunteers played an important role when creating training data for the algorithm. Notably, our results show that accurate, species-specific, automated counting of aerial wildlife images is now possible.",
"publicationTitle": "Methods in Ecology and Evolution",
"volume": "10",
"issue": "6",
"pages": "779-787",
"date": "2019",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "10.1111/2041-210X.13165",
"ISSN": "2041-210X",
"shortTitle": "",
"url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13165",
"accessDate": "2024-03-04T03:40:27Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Wiley Online Library",
"callNumber": "",
"rights": "© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society",
"extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13165",
"tags": [
{
"tag": "citizen science",
"type": 1
},
{
"tag": "conservation",
"type": 1
},
{
"tag": "deep learning",
"type": 1
},
{
"tag": "monitoring",
"type": 1
},
{
"tag": "population ecology",
"type": 1
},
{
"tag": "surveys",
"type": 1
}
],
"collections": [
"L3N9ZD5A"
],
"relations": {},
"dateAdded": "2024-03-04T03:40:27Z",
"dateModified": "2024-03-04T03:40:27Z"
}
},
{
"key": "9QZP3E7X",
"version": 5820,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/9QZP3E7X",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/9QZP3E7X",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/JE7LYPPT",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
}
},
"data": {
"key": "9QZP3E7X",
"version": 5820,
"parentItem": "JE7LYPPT",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Snapshot",
"accessDate": "2024-03-04T03:39:37Z",
"url": "https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13277",
"note": "",
"contentType": "text/html",
"charset": "utf-8",
"filename": "2041-210X.html",
"md5": "d6caaeac7af3c35548e31f87aca79a2b",
"mtime": 1709523577000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:39:37Z",
"dateModified": "2024-03-04T03:39:37Z"
}
},
{
"key": "58I9MG2N",
"version": 5821,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/58I9MG2N",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/58I9MG2N",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/JE7LYPPT",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "58I9MG2N",
"version": 5821,
"parentItem": "JE7LYPPT",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Full Text PDF",
"accessDate": "2024-03-04T03:39:29Z",
"url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13277",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Eikelboom et al. - 2019 - Improving the precision and accuracy of animal population estimates with aerial image object detecti.pdf",
"md5": "550a8e9dca1eb364af4618451426defb",
"mtime": 1709523569000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-04T03:39:29Z",
"dateModified": "2024-03-04T03:39:29Z"
}
},
{
"key": "JE7LYPPT",
"version": 5818,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/JE7LYPPT",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/JE7LYPPT",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Eikelboom et al.",
"parsedDate": "2019",
"numChildren": 2
},
"data": {
"key": "JE7LYPPT",
"version": 5818,
"itemType": "journalArticle",
"title": "Improving the precision and accuracy of animal population estimates with aerial image object detection",
"creators": [
{
"creatorType": "author",
"firstName": "Jasper A. J.",
"lastName": "Eikelboom"
},
{
"creatorType": "author",
"firstName": "Johan",
"lastName": "Wind"
},
{
"creatorType": "author",
"firstName": "Eline",
"lastName": "van de Ven"
},
{
"creatorType": "author",
"firstName": "Lekishon M.",
"lastName": "Kenana"
},
{
"creatorType": "author",
"firstName": "Bradley",
"lastName": "Schroder"
},
{
"creatorType": "author",
"firstName": "Henrik J.",
"lastName": "de Knegt"
},
{
"creatorType": "author",
"firstName": "Frank",
"lastName": "van Langevelde"
},
{
"creatorType": "author",
"firstName": "Herbert H. T.",
"lastName": "Prins"
}
],
"abstractNote": "Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. Aerial counts using cost-efficient Unmanned Aerial Vehicles or microlight aircrafts with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi-class convolutional neural network RetinaNet in detecting elephants, giraffes and zebras in aerial images from two Kenyan animal counts. The algorithm detected 95% of the number of elephants, 91% of giraffes and 90% of zebras that were found by four layers of human annotation, of which it correctly detected an extra 2.8% of elephants, 3.8% giraffes and 4.0% zebras that were missed by all humans, while detecting only 1.6 to 5.0 false positives per true positive. Furthermore, the animal detections by the algorithm were less sensitive to the sighting distance than humans were. With such a high recall and precision, we posit it is feasible to replace manual aerial animal count methods (from images and/or directly) by only the manual identification of image bounding boxes selected by the algorithm and then use a correction factor equal to the inverse of the undercounting bias in the calculation of the population estimates. This correction factor causes the standard error of the population estimate to increase slightly compared to a manual method, but this increase can be compensated for when the sampling effort would increase by 23%. However, an increase in sampling effort of 160% to 1,050% can be attained with the same expenses for equipment and personnel using our proposed semi-automatic method compared to a manual method. Therefore, we conclude that our proposed aerial count method will improve the accuracy of population estimates and will decrease the standard error of population estimates by 31% to 67%. Most importantly, this animal detection algorithm has the potential to outperform humans in detecting animals from the air when supplied with images taken at a fixed rate.",
"publicationTitle": "Methods in Ecology and Evolution",
"volume": "10",
"issue": "11",
"pages": "1875-1887",
"date": "2019",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "10.1111/2041-210X.13277",
"ISSN": "2041-210X",
"shortTitle": "",
"url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13277",
"accessDate": "2024-03-04T03:39:27Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Wiley Online Library",
"callNumber": "",
"rights": "© 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.",
"extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13277",
"tags": [
{
"tag": "computer vision",
"type": 1
},
{
"tag": "convolutional neural network",
"type": 1
},
{
"tag": "deep machine learning",
"type": 1
},
{
"tag": "drones",
"type": 1
},
{
"tag": "game census",
"type": 1
},
{
"tag": "image recognition",
"type": 1
},
{
"tag": "savanna",
"type": 1
},
{
"tag": "wildlife survey",
"type": 1
}
],
"collections": [
"L3N9ZD5A"
],
"relations": {},
"dateAdded": "2024-03-04T03:39:27Z",
"dateModified": "2024-03-04T03:39:27Z"
}
},
{
"key": "6BPHXDHF",
"version": 5815,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/6BPHXDHF",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/6BPHXDHF",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/KBBEPM4D",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "6BPHXDHF",
"version": 5815,
"parentItem": "KBBEPM4D",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Doll and Loos - Comparison of Object Detection Algorithms for Livestock Monitoring of Sheep in UAV images.pdf",
"accessDate": "2024-03-03T21:58:31Z",
"url": "https://publica-rest.fraunhofer.de/server/api/core/bitstreams/54e91b8d-569d-4902-97a4-26818911fcf6/content",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Doll and Loos - Comparison of Object Detection Algorithms for Livestock Monitoring of Sheep in UAV images.pdf",
"md5": "0665e556c83e23c9726c71f4a336f2c0",
"mtime": 1709503113000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-03T21:58:31Z",
"dateModified": "2024-03-03T21:58:33Z"
}
},
{
"key": "KBBEPM4D",
"version": 5814,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/KBBEPM4D",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/KBBEPM4D",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Doll and Loos",
"numChildren": 1
},
"data": {
"key": "KBBEPM4D",
"version": 5814,
"itemType": "journalArticle",
"title": "Comparison of Object Detection Algorithms for Livestock Monitoring of Sheep in UAV images",
"creators": [
{
"creatorType": "author",
"firstName": "Oliver",
"lastName": "Doll"
},
{
"creatorType": "author",
"firstName": "Alexander",
"lastName": "Loos"
}
],
"abstractNote": "This paper presents the EU funded project SPADE, a European initiative that aims to create an Intelligent Ecosystem utilizing unmanned aerial vehicles (UAVs) for delivering sustainable digital services to various end users in sectors like agriculture, forestry, and livestock. The project’s main goal is to cater to multiple purposes and benefit a wide range of stakeholders. In this paper we specifically concentrate on the livestock use-case and explore how state-of-the-art computer vision algorithms for object detection, tracking, and landscape classification, deployed on edge devices in drones, can offer researchers, conservationists, and farmers a non-intrusive, cost-effective, and efficient method for monitoring livestock increasing animal welfare, and optimize livestock management. We present initial findings by comparing the performance of different state-of-the-art object detectors on publicly available UAV images of sheep. The key performance metrics used are average precision, mean average precision and mean average recall. These findings should enable a better pre-selection of potential object detectors for the presented edge device use case.",
"publicationTitle": "",
"volume": "",
"issue": "",
"pages": "",
"date": "",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "",
"accessDate": "",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Zotero",
"callNumber": "",
"rights": "",
"extra": "",
"tags": [],
"collections": [
"L3N9ZD5A",
"C6X7FEXF"
],
"relations": {},
"dateAdded": "2024-03-03T21:58:33Z",
"dateModified": "2024-03-03T21:58:33Z"
}
},
{
"key": "DPLNTNKI",
"version": 5812,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/DPLNTNKI",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/DPLNTNKI",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/3DNVJUGS",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "DPLNTNKI",
"version": 5812,
"parentItem": "3DNVJUGS",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Full Text PDF",
"accessDate": "2024-03-03T21:53:27Z",
"url": "https://www.mdpi.com/1424-8220/20/7/2126/pdf?version=1586837549",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Barbedo et al. - 2020 - Counting Cattle in UAV Images—Dealing with Clustered Animals and AnimalBackground Contrast Changes.pdf",
"md5": "8780e4b1493c9863882e5fdd2dbebccc",
"mtime": 1709502807000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-03T21:53:27Z",
"dateModified": "2024-03-03T21:53:27Z"
}
},
{
"key": "3DNVJUGS",
"version": 5809,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/3DNVJUGS",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/3DNVJUGS",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Barbedo et al.",
"parsedDate": "2020-01",
"numChildren": 1
},
"data": {
"key": "3DNVJUGS",
"version": 5809,
"itemType": "journalArticle",
"title": "Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes",
"creators": [
{
"creatorType": "author",
"firstName": "Jayme Garcia Arnal",
"lastName": "Barbedo"
},
{
"creatorType": "author",
"firstName": "Luciano Vieira",
"lastName": "Koenigkan"
},
{
"creatorType": "author",
"firstName": "Patrícia Menezes",
"lastName": "Santos"
},
{
"creatorType": "author",
"firstName": "Andrea Roberto Bueno",
"lastName": "Ribeiro"
}
],
"abstractNote": "The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds.",
"publicationTitle": "Sensors",
"volume": "20",
"issue": "7",
"pages": "2126",
"date": "2020/1",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "10.3390/s20072126",
"ISSN": "1424-8220",
"shortTitle": "",
"url": "https://www.mdpi.com/1424-8220/20/7/2126",
"accessDate": "2024-03-03T21:53:24Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "www.mdpi.com",
"callNumber": "",
"rights": "http://creativecommons.org/licenses/by/3.0/",
"extra": "Number: 7\nPublisher: Multidisciplinary Digital Publishing Institute",
"tags": [
{
"tag": "Canchim breed",
"type": 1
},
{
"tag": "Nelore breed",
"type": 1
},
{
"tag": "convolutional neural networks",
"type": 1
},
{
"tag": "mathematical morphology",
"type": 1
},
{
"tag": "unmanned aerial vehicles",
"type": 1
}
],
"collections": [
"C6X7FEXF"
],
"relations": {},
"dateAdded": "2024-03-03T21:53:24Z",
"dateModified": "2024-03-03T21:53:24Z"
}
},
{
"key": "76WEIY2H",
"version": 5808,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/76WEIY2H",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/76WEIY2H",
"type": "text/html"
},
"up": {
"href": "https://api.zotero.org/groups/22818/items/L2LRDFCI",
"type": "application/json"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"numChildren": 0
},
"data": {
"key": "76WEIY2H",
"version": 5808,
"parentItem": "L2LRDFCI",
"itemType": "attachment",
"linkMode": "imported_url",
"title": "Full Text PDF",
"accessDate": "2024-03-03T21:53:15Z",
"url": "https://www.mdpi.com/2504-446X/4/4/75/pdf?version=1607504874",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "Barbedo et al. - 2020 - Cattle Detection Using Oblique UAV Images.pdf",
"md5": "1b7fda1481ad6ec38b0643d292197cd7",
"mtime": 1709502795000,
"tags": [],
"relations": {},
"dateAdded": "2024-03-03T21:53:15Z",
"dateModified": "2024-03-03T21:53:15Z"
}
},
{
"key": "L2LRDFCI",
"version": 5807,
"library": {
"type": "group",
"id": 22818,
"name": "Aerial Wildlife Survey",
"links": {
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/groups/22818/items/L2LRDFCI",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/L2LRDFCI",
"type": "text/html"
}
},
"meta": {
"createdByUser": {
"id": 6138136,
"username": "kirkl",
"name": "",
"links": {
"alternate": {
"href": "https://www.zotero.org/kirkl",
"type": "text/html"
}
}
},
"creatorSummary": "Barbedo et al.",
"parsedDate": "2020-12",
"numChildren": 1
},
"data": {
"key": "L2LRDFCI",
"version": 5807,
"itemType": "journalArticle",
"title": "Cattle Detection Using Oblique UAV Images",
"creators": [
{
"creatorType": "author",
"firstName": "Jayme Garcia Arnal",
"lastName": "Barbedo"
},
{
"creatorType": "author",
"firstName": "Luciano Vieira",
"lastName": "Koenigkan"
},
{
"creatorType": "author",
"firstName": "Patrícia Menezes",
"lastName": "Santos"
}
],
"abstractNote": "The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments were carried out: (1) five different sizes for the input images were tested to determine which yields the highest accuracies; (2) detection accuracies were calculated for different distances between animals and sensor, in order to determine how distance influences detectability; and (3) animals that were completely missed by the detection process were individually identified and the cause for those errors were determined, revealing some potential topics for further research. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.",
"publicationTitle": "Drones",
"volume": "4",
"issue": "4",
"pages": "75",
"date": "2020/12",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "10.3390/drones4040075",
"ISSN": "2504-446X",
"shortTitle": "",
"url": "https://www.mdpi.com/2504-446X/4/4/75",
"accessDate": "2024-03-03T21:53:13Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "www.mdpi.com",
"callNumber": "",
"rights": "http://creativecommons.org/licenses/by/3.0/",
"extra": "Number: 4\nPublisher: Multidisciplinary Digital Publishing Institute",
"tags": [
{
"tag": "convolutional neural network",
"type": 1
},
{
"tag": "deep learning",
"type": 1
},
{
"tag": "unmanned aerial vehicles",
"type": 1
}
],
"collections": [
"C6X7FEXF"
],
"relations": {},
"dateAdded": "2024-03-03T21:53:13Z",
"dateModified": "2024-03-03T21:53:13Z"
}
}
]