Autonomous Underwater Vehicle (AUV) carrying sonar for object detection has become one of the main ways of ocean exploration. However, object detection in sonar images always faces the problems of balancing detection accuracy and efficiency. To this end, this paper proposes an efficient underwater object detection network for sonar images named SDNet. In the model, the authors construct a new feature extraction network based on RepVGG to balance the detection accuracy and speed. By combining channel attention with RepVGG, useful information of high-order feature maps captured can be selectively paid attention to. Then, the authors design a feature fusion network to efficiently converge the location and semantic information of multi-scale feature maps. In the network, the authors propose a lightweight cross stage partial network for sonar (CSP_S) module suitable for sonar images, which can enhance the model's feature fusion capability and simplify the model. Finally, to reduce the conflict between classification and regression tasks, the authors leverage the Decoupled Head for the sonar object classification and localization. By testing on the self-built Underwater Sonar Dataset underwater sonar dataset (USD) and the public sonar dataset sonar common target detection dataset (SCTD), the detection accuracy of SDNet reaches 99.52% and 95.20%, respectively. Moreover, the detection speed reaches 114 frames per second (FPS) and 138 FPS, respectively. The experimental results show that SDNet can effectively balance the sonar detection accuracy and efficiency.

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