Effect of Seabed Type on Image Segmentation of an Underwater Object Obtained from a Side Scan Sonar Using a Deep Learning Approach

This study examines the impact of seabed conditions on image segmentation for seabed target images acquired via side-scan sonar during sea experiments. The dataset comprised cylindrical target images overlying on two seabed types, mud and sand, categorized accordingly. The deep learning algorithm (U-NET) was utilized for image segmentation. The analysis focused on two key factors influencing segmentation performance: the weighting method of the cross-entropy loss function and the combination of datasets categorized by seabed type for training, validation, and testing. The results revealed three key findings. First, applying equal weights to the loss function yielded better segmentation performance compared to pixel-frequency-based weighting. This improvement is indicated by Intersection over Union (IoU) for the highlight class in dataset 2 (0.41 compared to 0.37). Second, images from the mud area were easier to segment than those from the sand area. This was due to the clearer intensity contrast between the target highlight and background. This difference is indicated by the IoU for the highlight class (0.63 compared to 0.41). Finally, a network trained on a combined dataset from both seabed types improved segmentation performance. This improvement was observed in challenging conditions, such as sand areas. In comparison, a network trained on a single-seabed dataset showed lower performance. The IoU values for the highlight class in sand area images are as follows: 0.34 for training on mud, 0.41 for training on sand, and 0.45 for training on both.