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Although side-scan sonar can provide wide and high-resolution views of submarine terrain and objects, it suffers from severe interference due to complex environmental noise, variations in sonar configuration (such as frequency, beam pattern, etc.), and the small scale of targets, leading to a high misdetection rate. These challenges highlight the need for advanced detection models that can effectively address these limitations. Here, this paper introduces an enhanced YOLOv9(You Only Look Once v9) model named SOCA-YOLO, which integrates a Small Object focused Convolution module and an Attention mechanism to improve detection performance to tackle the challenges. The SOCA-YOLO framework first constructs a high-resolution SSS (sidescan sonar image) enhancement pipeline through image restoration techniques to extract fine-grained features of micro-scale targets. Subsequently, the SPDConv (Space-to-Depth Convolution) module is incorporated to optimize the feature extraction network, effectively preserving discriminative characteristics of small targets. Furthermore, the model integrates the standardized CBAM (Convolutional Block Attention Module) attention mechanism, enabling adaptive focus on salient regions of small targets in sonar images, thereby significantly improving detection robustness in complex underwater environments. Finally, the model is verified on a public side-scan sonar image dataset Cylinder2. Experiment results indicate that SOCA-YOLO achieves Precision and Recall at 71.8% and 72.7%, with an mAP50 of 74.3%. It outperforms the current state-of-the-art object detection method, YOLO11, as well as the original YOLOv9. Specifically, our model surpasses YOLO11 and YOLOv9 by 2.3% and 6.5% in terms of mAP50, respectively. Therefore, the SOCA-YOLO model provides a new and effective approach for small underwater object detection in side-scan sonar images.

Introduction: High-resolution sonar systems are critical for underwater robots to obtain precise environmental perception. However, the computational demands of processing sonar imagery in real-time pose significant challenges for autonomous underwater vehicles (AUVs) operating in dynamic environments. Current segmentation methods often struggle to balance processing speed with accuracy.

Introduction: Autonomous Underwater Vehicles (AUVs) are capable of independently performing underwater navigation tasks, with side-scan sonar being a primary tool for underwater detection. The integration of these two technologies enables autonomous monitoring of the marine environment.

Research vessel has multibeam sonar to survey the seafloor.

Calif. official: Tsunami damage upward of $40M.

Wreck of Cemfjord cargo ship found.

Wreck of Cemfjord cargo ship found.