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

التفاصيل البيبلوغرافية
العنوان: Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures
المؤلفون: Mengyu Tan, Wentao Chao, Jo-Ku Cheng, Mo Zhou, Yiwen Ma, Xinyi Jiang, Jianping Ge, Lian Yu, Limin Feng
المصدر: Animals, Vol 12, Iss 15, p 1976 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Veterinary medicine
LCC:Zoology
مصطلحات موضوعية: animal identification, camera trap, object detection, deep learning, Veterinary medicine, SF600-1100, Zoology, QL1-991
الوصف: Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-2615
Relation: https://www.mdpi.com/2076-2615/12/15/1976; https://doaj.org/toc/2076-2615
DOI: 10.3390/ani12151976
URL الوصول: https://doaj.org/article/26d894d7634f4c8b84109f41361d3fa3
رقم الأكسشن: edsdoj.26d894d7634f4c8b84109f41361d3fa3
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20762615
DOI:10.3390/ani12151976