YOLO-Animal: An efficient wildlife detection network based on improved YOLOv5

With the continuous development of modern society, the rapid expansion of human civilization squeezes the living space of other organisms, and the extinction of more and more biological species has sounded the alarm for us. Therefore, in order to timely understand the changes of wild animals and other resources in a specific area, and facilitate relevant personnel to formulate effective restoration and protection measures, this paper proposes a wild Animal species recognition network based on deep learning and improved YOLOv5: YOLO-Animal. The application of artificial intelligence and computer vision has covered all aspects of human’s daily life and work. The benchmark YOLOv5 algorithm can quickly and accurately deal with problems associated with images. Through the fusion of weighted Bidirectional Feature Pyramid Network (BiFPN) and Effective Channel Attention (ECA) module, the original YOLOv5s network structure is enhanced, and the detection accuracy of small targets and occluded and fuzzy targets is effectively improved. The YOLO-Animal model outperforms the benchmark YOLOv5s model by 3.2% on mAP and achieves 95.5% accuracy on the test set.