Dual Frequency Side Scan Sonar Image Fusion for Deep-Learning Based Underwater Target Detection

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 D²FNet (dual-domain fusion network). D²FNet 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, D²FNet significantly outperforms single-frequency and baseline fusion models in mAP metrics, demonstrating its effectiveness for high-precision underwater target detection.