Abstract: In order to solve the problem of low image resolution due to the degradation of underwater imaging environment and low detection accuracy caused by small fish targets,an improved YOLOv7 detection algorithm combining SPD-Conv structure and NAM attention mechanism is proposed.Firstly,the space-to-fepth(SPD) structure is used to improve the head network,which replaces the original straddle convolution structure in the network,retains more fine-grained information,improves the efficiency of feature learning,and improves the detection effect of the network on low-resolution images.Then,the normalization-based attention module(NAM) attention mechanism is introduced into the network,and the module integration method of CBAM is adopted,and the BN scaling factor is used to calculate the attention weight,which suppresses the insignificant features and improves the accuracy of small target detection.Finally,for underwater imaging degradation,the detection image is deconvolved and preprocessed,which reduces the impact of underwater imaging degradation factors on detection.Experimental results show that in the WildFish dataset,the overall accuracy of the model reaches 97.2%,which is 7.6% higher than that of the YOLOv7 algorithm,the accuracy rate is increased by 8.5%,and the recall rate is increased by 9.8%,compared with the Efficientdet,SSD,YOLOv5 and YOLOv8 algorithms,the accuracy of the proposed model is improved by 12.6%,17.8%,4% and 2.9%,respectively.The overall accuracy of the model reaches 80.5%,which is 18.4%,11.6%,6.9%,2.0% and 2.7% higher than that of Efficientdet,SSD,YOLOv5,YOLOv7 and YOLOv8,respectively,which can meet the needs of underwater fish identification.

Key words: Space-to-Depth Conv(SPD Conv), Normalization-based attention module(NAM), YOLOv7, Fish detection, Object detection