Shuffle-RDSNet: a method for side-scan sonar image classification with residual dual-path shrinkage network

Side-scan sonar (SSS) images have a wide range of applications in underwater target detection and recognition. However, due to the complexity of the underwater environment, the classification performance of sonar images is usually constrained by issues such as noise and inconspicuous texture features, including speckle noise, sensor noise, and interference from other sources. These noises can degrade the image quality, making it challenging to extract meaningful features and affecting the performance of classification algorithms. To address these challenges, we propose a novel classification model named Shuffle-RDSNet for SSS images. Specifically, we design the residual dual-path shrinkage network (RDSNet), which utilizes a soft thresholding function and determines the threshold by combining two paths to extract features from varying scales. The RDSNet is then integrated with the ShuffleNet V2 network to construct the proposed Shuffle-RDSNet model. This approach effectively mitigates the effect of noise in the feature extraction process and enhances the classification performance of the model. Furthermore, we employ additional techniques, including dilated convolution and depthwise separable convolution (DSC), to optimize the model and further enhance the classification accuracy and performance of the proposed method. Experimental results show that our model outperforms other classification models with a classification accuracy of up to 96.74% on the SSS image dataset, confirming the feasibility and effectiveness of using RDSNet for SSS image classification.