SSGA‐YOLO: A Lightweight Sonar Image Object Detection Network With Efficient Convolution and Acoustic‐Aware Attention for Embedded Systems

ABSTRACT To address the problems of high computational complexity and limited deploy ability in underwater sonar image detection, we propose SSGA‐YOLO, a lightweight and efficient object detection algorithm optimised for sonar imagery and successfully deployed on the Ascend AI embedded platform. SSGA‐YOLO focuses on three key aspects to achieve a favourable balance between accuracy and efficiency. The S‐Net backbone employs depthwise separable convolutions and a lightweight attention mechanism to enhance the extraction of weak echo and shadow features in sonar images while reducing redundancy for efficient deployment. The Efficient Group Shuffle Convolution (EGSConv) enhances cross‐channel feature interaction to improve the detection of small, low‐contrast sonar targets and the Lightweight Shuffle‐Aware Group Attention (LSGA) refines key acoustic and spatial cues in the presence of strong noise. Furthermore, SSGA‐YOLO significantly reduces model parameters and computational complexity: compared to YOLOv8n, it achieves reductions of 79.82% and 74.07% in parameter count and GFLOPs, respectively. To evaluate model performance across diverse environments, embedded deployment experiments were conducted on three datasets representing distinct scenarios: MDFD for controlled artificial tanks, UATD for complex natural waters and MOTfish for dynamic video sequences. SSGA‐YOLO consistently achieves high detection accuracy, with an mAP50 exceeding 0.930 on all datasets and peaking at 0.983 on MDFD. In terms of inference efficiency, the model demonstrates exceptional real‐time capability, reaching a frame rate of 65.77 FPS on MOTfish. These results outperform other lightweight detectors, confirming the model's effectiveness for practical underwater applications.