Benchmarking wild bird detection in complex forest scenes Article Swipe
Qi Song
,
Yu Guan
,
Xi Guo
,
Xinhui Guo
,
Yufeng Chen
,
Hongfang Wang
,
Jianping Ge
,
Tianming Wang
,
Lei Bao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2024.102466
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2024.102466
Camera traps are widely used for wildlife monitoring and making informed conservation and land-management decisions, but the resulting 'big data' are laborious to process. Deep learning-based methods have been adopted for wildlife detection in camera traps. However, these methods detect large mammals in uncomplicated scenes, where powerful deep-learning models work effectively. Few studies have been conducted to develop artificial intelligence for recognizing wild birds that live in complicated field scenes with protective colors and small sizes. Here we used a dataset of 9717 images from 15 bird species based on camera traps to test 8 object detection algorithms (Faster RCNN, Cascade RCNN, RetinaNet, FCOS, RepPoints, ATSS, Deformable-DETR, and Sparse RCNN) and assess their performance. We also explored the effect of different backbones on model accuracy. Among them, the Cascade RCNN model performs best, with a mAP of 0.693 in model capabilities. Models perform differently in certain species, and backbones significantly affect the accuracy of the model. Cascade RCNN utilizing the Swin-T backbone is the best-performing combination, with a mAP of 0.704. This study could help researchers identify birds efficiently and inspires research on wildlife recognition in complex ecological settings.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecoinf.2024.102466
- OA Status
- gold
- Cited By
- 20
- References
- 84
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4390732302Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecoinf.2024.102466Digital Object Identifier
- Title
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Benchmarking wild bird detection in complex forest scenesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-09Full publication date if available
- Authors
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Qi Song, Yu Guan, Xi Guo, Xinhui Guo, Yufeng Chen, Hongfang Wang, Jianping Ge, Tianming Wang, Lei BaoList of authors in order
- Landing page
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https://doi.org/10.1016/j.ecoinf.2024.102466Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ecoinf.2024.102466Direct OA link when available
- Concepts
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Benchmarking, Computer science, Artificial intelligence, Object detection, Wildlife, Camera trap, Field (mathematics), Deep learning, Machine learning, Random forest, Pattern recognition (psychology), Ecology, Biology, Marketing, Pure mathematics, Business, MathematicsTop concepts (fields/topics) attached by OpenAlex
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20Total citation count in OpenAlex
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2025: 8, 2024: 12Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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