DcNet: Dilated Convolutional Neural Networks for Side-Scan Sonar Image Semantic Segmentation

In ocean explorations, side-scan sonar (SSS) plays a very important role and can quickly depict seabed topography. Assembling the SSS to an autonomous underwater vehicle (AUV) and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition, which is conducive to submarine detection. However, because of the complexity of the marine environment, various noises in the ocean pollute the sonar image, which also encounters the intensity inhomogeneity problem. In this paper, we propose a novel neural network architecture named dilated convolutional neural network (DcNet) that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation. The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target, respectively. The core of our network is a novel block connection named DCblock, which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy. Furthermore, our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality images. We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets. Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures, the accuracy of our method is still comparable, which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.