ABSTRACT Side‐scan sonar is essential for underwater observation and seabed target detection, yet traditional single‐frequency systems must compromise between imaging resolution and detection range. For object detection tasks, high‐frequency sonar yields finer details contributed by stronger scattering and higher contrast but suffers from limited coverage due to heavier attenuation, whereas low‐frequency sonar covers wider areas yet offers poorer small‐target visibility. To overcome these trade‐offs and the challenges of strong speckle noise, target–shadow coupling and resolution variation, this study proposes a dual‐frequency detection model named D 2 FNet (dual‐domain fusion network). D 2 FNet integrates three key modules: (1) a union domain attention (UDA) module for preliminary dual‐frequency fusion via ResNet‐50 and a transformer encoder; (2) a cross‐domain attention (CDA) module for enhanced feature interaction across frequency domains; and (3) a target–shadow pairing (TSP) module that embeds sonar imaging priors through local window attention to suppress false alarms and improve localisation confidence. Based on a newly constructed dual‐frequency side‐scan sonar dataset containing over 9000 paired images from sea trials, D 2 FNet significantly outperforms single‐frequency and baseline fusion models in mAP metrics, demonstrating its effectiveness for high‐precision underwater target detection.
Xian et al. (Thu,) studied this question.