Underwater trash detection algorithm based on improved YOLOv5s

Aiming at the problem of insufficient storage space and limited computing ability of underwater mobile devices, an underwater garbage detection algorithm based on an improved YOLOv5s algorithm is proposed. The algorithm replaces the feature extraction module of the YOLOv5s network with the lightweight network MobileNetv3; the Convolutional Block Attention Module (CBAM) is embedded in the network to improve the feature extraction ability of the network in two dimensions of space and channel. At the same time, the improved network is pruned to reduce the redundant parameters and further compress the model. The experimental results show that the detection accuracy of the approach can reach 97.5% based on one-ninth of the parameters of YOLOv5s, and the real-time detection speed on the CPU is 2.5 times that of YOLOv5s.