YOLO-animal: an animal detection network based on improved YOLOv5 and attention mechanism

Abstract

Animal detection and recognition is a crucial task in computer vision. YOLOv5 has been widely used for animal identification in the past few years. However, it is still a challenging task due to the diverse array of animal types found in complex environments. In this paper, we introduce a new attention mechanism based on the CBAM attention mechanism to enhance the performance of the network model. Specifically, the attention mechanism enhances the interplay between globally pooled channel information, thereby bolstering the ability to detect and recognize animals with similar features in complex backgrounds. Experimental results on the Oxford-IIIT Pet validation dataset demonstrate the effectiveness of the proposed model's robustness and its ability to perform effectively in real-world scenarios.