Wildlife Recognition in Trap Images: Assessing the Efficiency of YOLO Frameworks

Identifying wildlife in their natural habitats is vital for ecological research, conservation, and biodiversity monitoring. Camera traps, which are remotely deployed devices designed to capture images of wildlife with minimal human interference are commonly used tools for this purpose. However, accurately detecting and identifying animals in these images is challenging due to factors such as camera angle, animal pose, background clutter, and lighting conditions. Object detection models, particularly those in the You Only Look Once (YOLO) family, have shown promise in addressing these challenges. YOLO models are known for their balance of speed and accuracy, making them well-suited for real-time applications. This study evaluates the efficacy of three YOLO model versions—YOLOv8, YOLOv9, and YOLOv10 for wildlife detection in camera trap images. The dataset consists of 16,489 images categorized into seven distinct wildlife species. The study aims to provide a comprehensive comparative analysis of these models based on key performance metrics: precision, recall, F1 Score, and mean Average Precision (mAP). The results reveal that YOLOv10 outperforms the other versions, achieving a precision of 0.956, a recall of 0.902, and a mAP of 0.675. These findings suggest that YOLOv10 is highly effective for detecting wildlife in this dataset. The study's results could significantly enhance wildlife monitoring and conservation strategies by improving the automated identification of animals in camera trap images.