How to achieve accurate wildlife detection by using vehicle‐mounted mobile monitoring images and deep learning? Article Swipe
Lei Shi
,
Jixi Gao
,
Fei Cao
,
Wenming Shen
,
Yue Wu
,
Kai Liu
,
Zheng Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/rse2.70003
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/rse2.70003
With the advancement of artificial intelligence (AI) technologies, vehicle‐mounted mobile monitoring systems have become increasingly integrated into wildlife monitoring practices. However, images captured through these systems often present challenges such as low resolution, small target sizes, and partial occlusions. Consequently, detecting animal targets using conventional deep‐learning networks is challenging. To address these challenges, this paper presents an enhanced YOLOv7 model, referred to as YOLOv7(sr‐sm), which incorporates a super‐resolution (SR) reconstruction module and a small object optimization module. The YOLOv7(sr‐sm) model introduces a super‐resolution reconstruction module that leverages generative adversarial networks (GANs) to reconstruct high‐resolution details from blurry animal images. Additionally, an attention mechanism is integrated into the Neck and Head of YOLOv7 to form a small object optimization module, which enhances the model's ability to detect and locate densely packed small targets. Using a vehicle‐mounted mobile monitoring system, images of four wildlife taxa—sheep, birds, deer, and antelope —were captured on the Tibetan Plateau. These images were combined with publicly available high‐resolution wildlife photographs to create a wildlife test dataset. Experiments were conducted on this dataset, comparing the YOLOv7(sr‐sm) model with eight popular object detection models. The results demonstrate significant improvements in precision, recall, and mean Average Precision (mAP), with YOLOv7(sr‐sm) achieving 93.9%, 92.1%, and 92.3%, respectively. Furthermore, compared to the newly released YOLOv8l model, YOLOv7(sr‐sm) outperforms it by 9.3%, 2.1%, and 4.5% in these three metrics while also exhibiting superior parameter efficiency and higher inference speeds. The YOLOv7(sr‐sm) model architecture can accurately locate and identify blurry animal targets in vehicle‐mounted monitoring images, serving as a reliable tool for animal identification and counting in mobile monitoring systems. These findings provide significant technological support for the application of intelligent monitoring techniques in biodiversity conservation efforts.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/rse2.70003
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003
- OA Status
- gold
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408466589
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408466589Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/rse2.70003Digital Object Identifier
- Title
-
How to achieve accurate wildlife detection by using vehicle‐mounted mobile monitoring images and deep learning?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-14Full publication date if available
- Authors
-
Lei Shi, Jixi Gao, Fei Cao, Wenming Shen, Yue Wu, Kai Liu, Zheng ZhangList of authors in order
- Landing page
-
https://doi.org/10.1002/rse2.70003Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Computer vision, Deep learning, Wildlife, Remote sensing, Geography, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4408466589 |
|---|---|
| doi | https://doi.org/10.1002/rse2.70003 |
| ids.doi | https://doi.org/10.1002/rse2.70003 |
| ids.openalex | https://openalex.org/W4408466589 |
| fwci | 0.0 |
| type | article |
| title | How to achieve accurate wildlife detection by using vehicle‐mounted mobile monitoring images and deep learning? |
| awards[0].id | https://openalex.org/G3907514898 |
| awards[0].funder_id | https://openalex.org/F4320335777 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2022YFF1300202 |
| awards[0].funder_display_name | National Key Research and Development Program of China |
| biblio.issue | 5 |
| biblio.volume | 11 |
| biblio.last_page | 538 |
| biblio.first_page | 524 |
| topics[0].id | https://openalex.org/T10199 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2303 |
| topics[0].subfield.display_name | Ecology |
| topics[0].display_name | Wildlife Ecology and Conservation |
| topics[1].id | https://openalex.org/T12388 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9973000288009644 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Identification and Quantification in Food |
| topics[2].id | https://openalex.org/T12644 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9944000244140625 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Wildlife-Road Interactions and Conservation |
| funders[0].id | https://openalex.org/F4320335777 |
| funders[0].ror | |
| funders[0].display_name | National Key Research and Development Program of China |
| is_xpac | False |
| apc_list.value | 1921 |
| apc_list.currency | USD |
| apc_list.value_usd | 1921 |
| apc_paid.value | 1921 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1921 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.5927319526672363 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5480915307998657 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C31972630 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5308187007904053 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[2].display_name | Computer vision |
| concepts[3].id | https://openalex.org/C108583219 |
| concepts[3].level | 2 |
| concepts[3].score | 0.46183252334594727 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[3].display_name | Deep learning |
| concepts[4].id | https://openalex.org/C29376679 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4456171989440918 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q241741 |
| concepts[4].display_name | Wildlife |
| concepts[5].id | https://openalex.org/C62649853 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3946836590766907 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[5].display_name | Remote sensing |
| concepts[6].id | https://openalex.org/C205649164 |
| concepts[6].level | 0 |
| concepts[6].score | 0.2391698658466339 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[6].display_name | Geography |
| concepts[7].id | https://openalex.org/C86803240 |
| concepts[7].level | 0 |
| concepts[7].score | 0.12077683210372925 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[7].display_name | Biology |
| concepts[8].id | https://openalex.org/C18903297 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[8].display_name | Ecology |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.5927319526672363 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5480915307998657 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/computer-vision |
| keywords[2].score | 0.5308187007904053 |
| keywords[2].display_name | Computer vision |
| keywords[3].id | https://openalex.org/keywords/deep-learning |
| keywords[3].score | 0.46183252334594727 |
| keywords[3].display_name | Deep learning |
| keywords[4].id | https://openalex.org/keywords/wildlife |
| keywords[4].score | 0.4456171989440918 |
| keywords[4].display_name | Wildlife |
| keywords[5].id | https://openalex.org/keywords/remote-sensing |
| keywords[5].score | 0.3946836590766907 |
| keywords[5].display_name | Remote sensing |
| keywords[6].id | https://openalex.org/keywords/geography |
| keywords[6].score | 0.2391698658466339 |
| keywords[6].display_name | Geography |
| keywords[7].id | https://openalex.org/keywords/biology |
| keywords[7].score | 0.12077683210372925 |
| keywords[7].display_name | Biology |
| language | en |
| locations[0].id | doi:10.1002/rse2.70003 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2737234030 |
| locations[0].source.issn | 2056-3485 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2056-3485 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Remote Sensing in Ecology and Conservation |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_lineage_names | Wiley |
| locations[0].license | |
| locations[0].pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Remote Sensing in Ecology and Conservation |
| locations[0].landing_page_url | https://doi.org/10.1002/rse2.70003 |
| locations[1].id | pmh:oai:doaj.org/article:1383d643796c4d99acfa8080c12a7d49 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Remote Sensing in Ecology and Conservation, Vol 11, Iss 5, Pp 524-538 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/1383d643796c4d99acfa8080c12a7d49 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5014936207 |
| authorships[0].author.orcid | https://orcid.org/0009-0002-8383-0757 |
| authorships[0].author.display_name | Lei Shi |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[0].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210137199 |
| authorships[0].affiliations[1].raw_affiliation_string | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China |
| authorships[0].institutions[0].id | https://openalex.org/I4210137199 |
| authorships[0].institutions[0].ror | https://ror.org/0419fj215 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210137199 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Aerospace Information Research Institute |
| authorships[0].institutions[1].id | https://openalex.org/I19820366 |
| authorships[0].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[0].institutions[1].type | government |
| authorships[0].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[0].institutions[2].id | https://openalex.org/I204710742 |
| authorships[0].institutions[2].ror | https://ror.org/04gwbew76 |
| authorships[0].institutions[2].type | government |
| authorships[0].institutions[2].lineage | https://openalex.org/I204710742 |
| authorships[0].institutions[2].country_code | CN |
| authorships[0].institutions[2].display_name | Ministry of Ecology and Environment |
| authorships[0].institutions[3].id | https://openalex.org/I4210158498 |
| authorships[0].institutions[3].ror | https://ror.org/04x9p9g72 |
| authorships[0].institutions[3].type | government |
| authorships[0].institutions[3].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[0].institutions[3].country_code | CN |
| authorships[0].institutions[3].display_name | Satellite Application Center for Ecology and Environment |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Leilei Shi |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China, Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[1].author.id | https://openalex.org/A5084904445 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9249-6968 |
| authorships[1].author.display_name | Jixi Gao |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[1].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[1].institutions[0].id | https://openalex.org/I204710742 |
| authorships[1].institutions[0].ror | https://ror.org/04gwbew76 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I204710742 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Ministry of Ecology and Environment |
| authorships[1].institutions[1].id | https://openalex.org/I4210158498 |
| authorships[1].institutions[1].ror | https://ror.org/04x9p9g72 |
| authorships[1].institutions[1].type | government |
| authorships[1].institutions[1].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Satellite Application Center for Ecology and Environment |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jixi Gao |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[2].author.id | https://openalex.org/A5101983212 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2103-0957 |
| authorships[2].author.display_name | Fei Cao |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[2].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[2].institutions[0].id | https://openalex.org/I204710742 |
| authorships[2].institutions[0].ror | https://ror.org/04gwbew76 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I204710742 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Ministry of Ecology and Environment |
| authorships[2].institutions[1].id | https://openalex.org/I4210158498 |
| authorships[2].institutions[1].ror | https://ror.org/04x9p9g72 |
| authorships[2].institutions[1].type | government |
| authorships[2].institutions[1].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Satellite Application Center for Ecology and Environment |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Fei Cao |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[3].author.id | https://openalex.org/A5108640289 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Wenming Shen |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[3].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[3].institutions[0].id | https://openalex.org/I204710742 |
| authorships[3].institutions[0].ror | https://ror.org/04gwbew76 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I204710742 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Ministry of Ecology and Environment |
| authorships[3].institutions[1].id | https://openalex.org/I4210158498 |
| authorships[3].institutions[1].ror | https://ror.org/04x9p9g72 |
| authorships[3].institutions[1].type | government |
| authorships[3].institutions[1].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Satellite Application Center for Ecology and Environment |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Wenming Shen |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[4].author.id | https://openalex.org/A5101983894 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-3236-1818 |
| authorships[4].author.display_name | Yue Wu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[4].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[4].institutions[0].id | https://openalex.org/I204710742 |
| authorships[4].institutions[0].ror | https://ror.org/04gwbew76 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I204710742 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Ministry of Ecology and Environment |
| authorships[4].institutions[1].id | https://openalex.org/I4210158498 |
| authorships[4].institutions[1].ror | https://ror.org/04x9p9g72 |
| authorships[4].institutions[1].type | government |
| authorships[4].institutions[1].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Satellite Application Center for Ecology and Environment |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yue Wu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[5].author.id | https://openalex.org/A5101542636 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5927-4327 |
| authorships[5].author.display_name | Kai Liu |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210137199 |
| authorships[5].affiliations[0].raw_affiliation_string | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China |
| authorships[5].institutions[0].id | https://openalex.org/I4210137199 |
| authorships[5].institutions[0].ror | https://ror.org/0419fj215 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210137199 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Aerospace Information Research Institute |
| authorships[5].institutions[1].id | https://openalex.org/I19820366 |
| authorships[5].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[5].institutions[1].type | government |
| authorships[5].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Kai Liu |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China |
| authorships[6].author.id | https://openalex.org/A5100459067 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7063-8390 |
| authorships[6].author.display_name | Zheng Zhang |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[6].affiliations[0].raw_affiliation_string | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| authorships[6].institutions[0].id | https://openalex.org/I204710742 |
| authorships[6].institutions[0].ror | https://ror.org/04gwbew76 |
| authorships[6].institutions[0].type | government |
| authorships[6].institutions[0].lineage | https://openalex.org/I204710742 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Ministry of Ecology and Environment |
| authorships[6].institutions[1].id | https://openalex.org/I4210158498 |
| authorships[6].institutions[1].ror | https://ror.org/04x9p9g72 |
| authorships[6].institutions[1].type | government |
| authorships[6].institutions[1].lineage | https://openalex.org/I204710742, https://openalex.org/I4210158498 |
| authorships[6].institutions[1].country_code | CN |
| authorships[6].institutions[1].display_name | Satellite Application Center for Ecology and Environment |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zheng Zhang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Key Laboratory of Satellite Remote Sensing, Satellite Environmental Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094 China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | How to achieve accurate wildlife detection by using vehicle‐mounted mobile monitoring images and deep learning? |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10199 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2303 |
| primary_topic.subfield.display_name | Ecology |
| primary_topic.display_name | Wildlife Ecology and Conservation |
| related_works | https://openalex.org/W2731899572, https://openalex.org/W3215138031, https://openalex.org/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1002/rse2.70003 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2737234030 |
| best_oa_location.source.issn | 2056-3485 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2056-3485 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Remote Sensing in Ecology and Conservation |
| best_oa_location.source.host_organization | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_name | Wiley |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_lineage_names | Wiley |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Remote Sensing in Ecology and Conservation |
| best_oa_location.landing_page_url | https://doi.org/10.1002/rse2.70003 |
| primary_location.id | doi:10.1002/rse2.70003 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2737234030 |
| primary_location.source.issn | 2056-3485 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2056-3485 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Remote Sensing in Ecology and Conservation |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_lineage_names | Wiley |
| primary_location.license | |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rse2.70003 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Remote Sensing in Ecology and Conservation |
| primary_location.landing_page_url | https://doi.org/10.1002/rse2.70003 |
| publication_date | 2025-03-14 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4386779901, https://openalex.org/W4213309796, https://openalex.org/W2964241181, https://openalex.org/W1982025193, https://openalex.org/W2765811365, https://openalex.org/W2754769271, https://openalex.org/W2100135944, https://openalex.org/W2964298670, https://openalex.org/W2569272946, https://openalex.org/W3004612232, https://openalex.org/W2625219738, https://openalex.org/W2769210209, https://openalex.org/W2498805680, https://openalex.org/W4389280647, https://openalex.org/W2187614429, https://openalex.org/W2900884109, https://openalex.org/W4289986171, https://openalex.org/W2982770724, https://openalex.org/W4386076325, https://openalex.org/W4312441525 |
| referenced_works_count | 20 |
| abstract_inverted_index.a | 67, 73, 82, 115, 134, 166, 255 |
| abstract_inverted_index.To | 50 |
| abstract_inverted_index.an | 57, 101 |
| abstract_inverted_index.as | 31, 63, 254 |
| abstract_inverted_index.by | 218 |
| abstract_inverted_index.in | 191, 223, 249, 263, 280 |
| abstract_inverted_index.is | 48, 104 |
| abstract_inverted_index.it | 217 |
| abstract_inverted_index.of | 4, 111, 140, 276 |
| abstract_inverted_index.on | 150, 173 |
| abstract_inverted_index.to | 62, 92, 113, 125, 164, 209 |
| abstract_inverted_index.The | 78, 186, 237 |
| abstract_inverted_index.and | 37, 72, 109, 127, 146, 194, 204, 221, 233, 244, 261 |
| abstract_inverted_index.can | 241 |
| abstract_inverted_index.for | 258, 273 |
| abstract_inverted_index.low | 32 |
| abstract_inverted_index.the | 2, 107, 122, 151, 177, 210, 274 |
| abstract_inverted_index.(AI) | 7 |
| abstract_inverted_index.(SR) | 69 |
| abstract_inverted_index.4.5% | 222 |
| abstract_inverted_index.Head | 110 |
| abstract_inverted_index.Neck | 108 |
| abstract_inverted_index.With | 1 |
| abstract_inverted_index.also | 228 |
| abstract_inverted_index.form | 114 |
| abstract_inverted_index.four | 141 |
| abstract_inverted_index.from | 96 |
| abstract_inverted_index.have | 13 |
| abstract_inverted_index.into | 17, 106 |
| abstract_inverted_index.mean | 195 |
| abstract_inverted_index.such | 30 |
| abstract_inverted_index.test | 168 |
| abstract_inverted_index.that | 86 |
| abstract_inverted_index.this | 54, 174 |
| abstract_inverted_index.tool | 257 |
| abstract_inverted_index.were | 156, 171 |
| abstract_inverted_index.with | 158, 180, 199 |
| abstract_inverted_index.2.1%, | 220 |
| abstract_inverted_index.9.3%, | 219 |
| abstract_inverted_index.These | 154, 267 |
| abstract_inverted_index.Using | 133 |
| abstract_inverted_index.deer, | 145 |
| abstract_inverted_index.eight | 181 |
| abstract_inverted_index.model | 80, 179, 239 |
| abstract_inverted_index.newly | 211 |
| abstract_inverted_index.often | 27 |
| abstract_inverted_index.paper | 55 |
| abstract_inverted_index.small | 34, 74, 116, 131 |
| abstract_inverted_index.these | 25, 52, 224 |
| abstract_inverted_index.three | 225 |
| abstract_inverted_index.using | 44 |
| abstract_inverted_index.which | 65, 120 |
| abstract_inverted_index.while | 227 |
| abstract_inverted_index.(GANs) | 91 |
| abstract_inverted_index.(mAP), | 198 |
| abstract_inverted_index.92.1%, | 203 |
| abstract_inverted_index.92.3%, | 205 |
| abstract_inverted_index.93.9%, | 202 |
| abstract_inverted_index.YOLOv7 | 59, 112 |
| abstract_inverted_index.animal | 42, 98, 247, 259 |
| abstract_inverted_index.become | 14 |
| abstract_inverted_index.birds, | 144 |
| abstract_inverted_index.blurry | 97, 246 |
| abstract_inverted_index.create | 165 |
| abstract_inverted_index.detect | 126 |
| abstract_inverted_index.higher | 234 |
| abstract_inverted_index.images | 22, 139, 155 |
| abstract_inverted_index.locate | 128, 243 |
| abstract_inverted_index.mobile | 10, 136, 264 |
| abstract_inverted_index.model, | 60, 214 |
| abstract_inverted_index.module | 71, 85 |
| abstract_inverted_index.object | 75, 117, 183 |
| abstract_inverted_index.packed | 130 |
| abstract_inverted_index.sizes, | 36 |
| abstract_inverted_index.target | 35 |
| abstract_inverted_index.Average | 196 |
| abstract_inverted_index.Tibetan | 152 |
| abstract_inverted_index.YOLOv8l | 213 |
| abstract_inverted_index.ability | 124 |
| abstract_inverted_index.address | 51 |
| abstract_inverted_index.densely | 129 |
| abstract_inverted_index.details | 95 |
| abstract_inverted_index.images, | 252 |
| abstract_inverted_index.images. | 99 |
| abstract_inverted_index.metrics | 226 |
| abstract_inverted_index.model's | 123 |
| abstract_inverted_index.models. | 185 |
| abstract_inverted_index.module, | 119 |
| abstract_inverted_index.module. | 77 |
| abstract_inverted_index.partial | 38 |
| abstract_inverted_index.popular | 182 |
| abstract_inverted_index.present | 28 |
| abstract_inverted_index.provide | 269 |
| abstract_inverted_index.recall, | 193 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.serving | 253 |
| abstract_inverted_index.speeds. | 236 |
| abstract_inverted_index.support | 272 |
| abstract_inverted_index.system, | 138 |
| abstract_inverted_index.systems | 12, 26 |
| abstract_inverted_index.targets | 43, 248 |
| abstract_inverted_index.through | 24 |
| abstract_inverted_index.—were | 148 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 21 |
| abstract_inverted_index.Plateau. | 153 |
| abstract_inverted_index.antelope | 147 |
| abstract_inverted_index.captured | 23, 149 |
| abstract_inverted_index.combined | 157 |
| abstract_inverted_index.compared | 208 |
| abstract_inverted_index.counting | 262 |
| abstract_inverted_index.dataset, | 175 |
| abstract_inverted_index.dataset. | 169 |
| abstract_inverted_index.efforts. | 283 |
| abstract_inverted_index.enhanced | 58 |
| abstract_inverted_index.enhances | 121 |
| abstract_inverted_index.findings | 268 |
| abstract_inverted_index.identify | 245 |
| abstract_inverted_index.networks | 47, 90 |
| abstract_inverted_index.presents | 56 |
| abstract_inverted_index.publicly | 159 |
| abstract_inverted_index.referred | 61 |
| abstract_inverted_index.released | 212 |
| abstract_inverted_index.reliable | 256 |
| abstract_inverted_index.superior | 230 |
| abstract_inverted_index.systems. | 266 |
| abstract_inverted_index.targets. | 132 |
| abstract_inverted_index.wildlife | 18, 142, 162, 167 |
| abstract_inverted_index.Precision | 197 |
| abstract_inverted_index.achieving | 201 |
| abstract_inverted_index.attention | 102 |
| abstract_inverted_index.available | 160 |
| abstract_inverted_index.comparing | 176 |
| abstract_inverted_index.conducted | 172 |
| abstract_inverted_index.detecting | 41 |
| abstract_inverted_index.detection | 184 |
| abstract_inverted_index.inference | 235 |
| abstract_inverted_index.leverages | 87 |
| abstract_inverted_index.mechanism | 103 |
| abstract_inverted_index.parameter | 231 |
| abstract_inverted_index.accurately | 242 |
| abstract_inverted_index.artificial | 5 |
| abstract_inverted_index.challenges | 29 |
| abstract_inverted_index.efficiency | 232 |
| abstract_inverted_index.exhibiting | 229 |
| abstract_inverted_index.generative | 88 |
| abstract_inverted_index.integrated | 16, 105 |
| abstract_inverted_index.introduces | 81 |
| abstract_inverted_index.monitoring | 11, 19, 137, 251, 265, 278 |
| abstract_inverted_index.practices. | 20 |
| abstract_inverted_index.precision, | 192 |
| abstract_inverted_index.techniques | 279 |
| abstract_inverted_index.Experiments | 170 |
| abstract_inverted_index.advancement | 3 |
| abstract_inverted_index.adversarial | 89 |
| abstract_inverted_index.application | 275 |
| abstract_inverted_index.challenges, | 53 |
| abstract_inverted_index.demonstrate | 188 |
| abstract_inverted_index.intelligent | 277 |
| abstract_inverted_index.occlusions. | 39 |
| abstract_inverted_index.outperforms | 216 |
| abstract_inverted_index.photographs | 163 |
| abstract_inverted_index.reconstruct | 93 |
| abstract_inverted_index.resolution, | 33 |
| abstract_inverted_index.significant | 189, 270 |
| abstract_inverted_index.Furthermore, | 207 |
| abstract_inverted_index.architecture | 240 |
| abstract_inverted_index.biodiversity | 281 |
| abstract_inverted_index.challenging. | 49 |
| abstract_inverted_index.conservation | 282 |
| abstract_inverted_index.conventional | 45 |
| abstract_inverted_index.improvements | 190 |
| abstract_inverted_index.incorporates | 66 |
| abstract_inverted_index.increasingly | 15 |
| abstract_inverted_index.intelligence | 6 |
| abstract_inverted_index.optimization | 76, 118 |
| abstract_inverted_index.Additionally, | 100 |
| abstract_inverted_index.Consequently, | 40 |
| abstract_inverted_index.respectively. | 206 |
| abstract_inverted_index.taxa—sheep, | 143 |
| abstract_inverted_index.technological | 271 |
| abstract_inverted_index.technologies, | 8 |
| abstract_inverted_index.identification | 260 |
| abstract_inverted_index.reconstruction | 70, 84 |
| abstract_inverted_index.YOLOv7(sr‐sm) | 79, 178, 200, 215, 238 |
| abstract_inverted_index.deep‐learning | 46 |
| abstract_inverted_index.YOLOv7(sr‐sm), | 64 |
| abstract_inverted_index.high‐resolution | 94, 161 |
| abstract_inverted_index.vehicle‐mounted | 9, 135, 250 |
| abstract_inverted_index.super‐resolution | 68, 83 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5014936207 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I204710742, https://openalex.org/I4210137199, https://openalex.org/I4210158498 |
| citation_normalized_percentile.value | 0.0802862 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |