Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learning

Recently, deep learning model has received substantial interest in industrial applications [4, 5]. In the vision-based An automatic vision-based sewer inspection plays a key sewer inspection community, deep learning also attracts increasing role of sewage system in a modern city. Recent advances focus attention from both academia and industry [6, 7, 8]. on utilizing deep learning model to realize the sewer inspection Here, we focus on the sewer defect classification in the setting system, benefiting from the capability of data-driven of multi-label, in which multiply defect classes in an feature representation. However, the inherent uncertainty of image are recognized simultaneously. Although these deep sewer defects is ignored, resulting in the missed detection learning-based methods have achieved acceptable performances of serious unknown sewer defect categories. In this paper, of sewer defect classification, while the inherent uncertainty we propose a trustworthy multi-label sewer defect classification of sewer defects might not be considered sufficiently (TMSDC) method, which can quantify the uncertainty of in real-world applications [9].