A Real-Time Fish Target Detection Algorithm Based on Improved YOLOv5

Abstract

Marine fish target detection technology is of great significance for underwater vehicles to realize fish automatic recognition. However, the complex underwater environment and lighting conditions lead to the complex background of the collected image and more irrelevant interference, which makes the fish target detection more difficult. In order to detect fish targets accurately and quickly, a real-time fish target detection network based on improved YOLOv5s is proposed. Firstly, the Gamma transform is introduced in the preprocessing part to improve the gray and contrast of the marine fish image, which is convenient for model detection. Secondly, the ShuffleNetv2 lightweight network introducing the SE channel attention mechanism is used to replace the original backbone network CSPDarkNet53 of YOLOv5 to reduce the model size and the amount of calculation, and speed up the detection. Finally, the improved BiFPN-Short network is used to replace the PANet network for feature fusion, so as to enhance the information propagation between different levels and improve the accuracy of the detection algorithm. Experimental results show that the volume of the improved model is reduced by 76.64%, the number of parameters is reduced by 81.60%, the floating-point operations (FLOPs) is decreased by 81.22% and the mean average precision (mAP) is increased to 98.10%. The balance between lightweight and detection accuracy is achieved, and this paper also provides a reference for the development of underwater target detection equipment.