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

arXiv: https://arxiv.org/abs/2210.13782 · HTML: https://mlsanity.com/paper/2210.13782/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [TResNet: High Performance GPU-Dedicated Architecture](https://mlsanity.com/paper/2003.13630/) (2020)
- [Can multi-label classification networks know what they don't know?](https://mlsanity.com/paper/2109.14162/) (2021)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark](https://mlsanity.com/paper/2103.10895/) (2021)
- [SEED: Sound Event Early Detection via Evidential Uncertainty](https://mlsanity.com/paper/2202.02441/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [Multi-label out-of-distribution detection via evidential learning](https://mlsanity.com/paper/2502.18224/) (2025)
- [Knowledge Distillation Under Ideal Joint Classifier Assumption](https://mlsanity.com/paper/2304.11004/) (2023)
- [Dual-Stream Attention Transformers for Sewer Defect Classification](https://mlsanity.com/paper/2311.16145/) (2023)
- [Self-Supervised Learning for Identifying Defects in Sewer Footage](https://mlsanity.com/paper/2409.02140/) (2024)

## Abstract (arXiv)

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.