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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection

Mengyu Tan¹, Wentao Chao², Jo-Ku Cheng³

¹Ministry of Education Key Laboratory for Biodiversity Science and Engineering, National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard National Park, Northeast Tiger and Leopard Biodiversity National Observation and Research Station, National Forestry and Grassland Administration Amur Tiger and Amur Leopard Monitoring and Research Center, College of Life Sciences, Beijing Normal University, Beijing 100875, China.

Animals : an Open Access Journal From MDPI

|August 12, 2022

Summary

This summary is machine-generated.

Deep learning models accurately detect wildlife in camera trap images, with YOLOv5m showing the best performance. AI significantly speeds up ecological data analysis from large image datasets.

Keywords:

animal identification camera trap deep learning object detection

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Area of Science:

Ecology
Computer Science
Artificial Intelligence

Background:

Camera traps generate vast image/video data for wildlife monitoring.
Manual analysis is time-consuming, necessitating automated solutions.
Deep learning shows promise for automated wildlife identification in camera trap imagery.

Purpose of the Study:

To evaluate and compare deep learning object detection models for wildlife in camera trap data.
To assess model performance using a new dataset from Northeast Tiger and Leopard National Park (NTLNP).
To compare training models on day/night data separately versus combined.

Main Methods:

Constructed the NTLNP wildlife image dataset.
Evaluated YOLOv5, Cascade R-CNN (HRNet32), and FCOS (ResNet50/101) object detection models.
Trained and tested models on combined day and night imagery.

Main Results:

Day-night joint training yielded satisfying results for object detection models.
Achieved 0.98 mean average precision (mAP) for animal image detection.
Reached 88% accuracy for animal video classification, with YOLOv5m as the top performer.

Conclusions:

Deep learning, particularly YOLOv5m, offers efficient and accurate wildlife detection in camera trap data.
AI significantly reduces manual effort in analyzing large ecological image datasets.
Joint day-night training improves model performance for comprehensive wildlife monitoring.