Trusted Video-Based Sewer Inspection via Support Clip-Based Pareto-Optimal Evidential Network

Chenyang Zhao, Chuanfei Hu, Hang Shao, Fir Dunkin, Yongxiong Wang

Abstract: An automatic vision-based sewer inspection plays a vital role of sewage system in a modern city. Existing methods have utilized evidential deep learning to construct trusted models. Although the acceptable performance has been achieved in sewer defect classification, the fine-grained information of sewer defects in videos is ignored. Meanwhile, the trade-off between multi-label classification and uncertainty estimation remains challenging. In this paper, support clip-based pareto-optimal evidential network (POEN) is proposed for trusted video-based sewer inspection. Specifically, support clip module (SCM) is designed to capture the fine-grained visual representation of defects from local scale segments. Then, evidential deep learning is introduced to quantify the uncertainty for out-of-distribution detection. Furthermore, Pareto-optimal weighting scheme (PWS) is designed to solve the common trade-off dilemma in multi-task learning. Extensive experiments are conducted on VideoPipe, in which the superiority of POEN is demonstrated compared with the state-of-the-art methods.