Abstract: Aiming at the problems of serious image degradation and low target recognition rate in current underwater vehicle target detection methods, an underwater target detection method combining improved RetinaNet and attention mechanism is proposed. Firstly, RetinaNet backbone network is replaced with DenseNet network, which retains more target features and reduces the number of parameters. Secondly, in order to increase the operation speed of the network model, the initial convolution is replaced by the depth separated deformable convolution, thus greatly reducing the parameters of the model. Finally, CBAM attention module is introduced to enhance features in space and channel dimensions, reducing the interference of underwater complex environment to target detection. The experimental results of underwater robot grasping show that compared with the initial RetinaNet methods, The mAP value of the improved method can reach 81.9%, the parameters are 56.8 MB, and the detection speed is 16.8 frames. The improved method has excellent performance in underwater target detection.

Key words: underwater target detection, RetinaNet, lightweight network, attention mechanism