Abstract
Due to the weak structural features of pipelines and underwater light attenuation, the complex and harsh environment of the seabed greatly increases the possibility of an underwater autonomous remotely operated vehicle losing a detected seabed-contacting segment during pipe-laying operations. To address this problem, we propose a cascade attention module and a prefusion module with a convolutional neural network. The cascade attention module samples the feature maps in a non-convolutional form to realize the interaction between structure and channels, and the attention map is generated by cascading attention. The prefusion module pre-fuses the three layers of feature maps from different stages in the backbone, and the delicate features of the shallow feature maps are fused with the deeper feature maps to generate richer feature maps with space location and semantic classification information. We conduct experiments to verify our modules, both on the underwater pipeline dataset and in a tank test. The results show that our modules can improve the performance of different neural network models for seabed-contacting segment detection. The target detection and instance segmentation performance of the best model is improved through a 6.3% increase in AP and a 3.8% increase in mean intersection over union compared with the baseline model.