Camouflaged Animal Target Detection Algorithm Based on YOLOv8

It is not easy to quickly distinguish mimetic camouflaged animals by human eyes. The YOLO series of algorithms in the field of deep learning can be used to quickly detect camouflaged animal targets in an image. This thesis use dataset that downloaded and integrated from the official YOLV8 dataset websiteto train the model based on YOLOv5 and YOLOv8. The experimental results show that the mAP value of YOLOv5 is 84.77%, and YOLOv8's mAP value is 81.05%. The two trained models are also applied to detect camouflage animals in Changbai Mountain area, and the detection results are evaluated and analyzed. The detection effects of the trained YOLOv5 and YOLOv8 models are also compared and analyzed to provide some references for the future camouflage animal target detection field.