A multi-task segmentation and classification network for remote ship hull inspection

Abstract

In contrast to manual close-up ship hull inspection methods, Remote Inspection Technology (RIT) offers the potential to improve performance while minimising costs. Nevertheless, the effectiveness of the RIT is still subject to the experience and expertise of the inspectors. As a result, development has focused on applying automated data processing methods to RIT itself. However, in the hull inspection scenario, several challenges remain, including suboptimal hull imaging conditions, disparities in the distribution of defect categories, and significant variations in defect size. To overcome these challenges, we introduce a multi-task hull inspection network (MTHI-Net) that leverages the principles of multi-task learning to improve hull inspection accuracy. This network addresses two problems associated with RIT: image-level defect classification and pixel-level defect segmentation. Specifically, MTHI-Net exploits the advantages of spatial and channel self-attention mechanisms, a residual refinement module, and a feature fusion module to enhance the feature representation capability for defect classification and mitigate the probability of mis-segmentation. In addition, a lightweight MTHI-Net called MTHI-Net-Lite was developed. The experimental results show that the proposed networks fulfil the hull inspection task better than the baselines in both real underwater and in-dock RIT scenarios.