Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learning
This work addresses the issue of missed detection in sewer inspection systems for urban infrastructure management, but it is incremental as it builds on existing deep learning approaches with added uncertainty quantification.
The paper tackles the problem of uncertainty in automatic sewer defect classification by proposing a trustworthy multi-label method that quantifies uncertainty using evidential deep learning and incorporates expert knowledge, achieving superior uncertainty estimation on a public benchmark.
An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark.