论文标题
通过可信赖的多标签下水道缺陷分类通过证据深度学习
Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learning
论文作者
论文摘要
基于自动视觉的下水道检查在现代城市中起着污水系统的关键作用。最近的进步着重于利用深度学习模型实现下水道检查系统，从数据驱动的功能表示的能力中受益。但是，下水道缺陷的固有不确定性被忽略了，导致未知的未知下水道缺陷类别缺失。在本文中，我们提出了可信赖的多标签下水道缺陷分类（TMSDC）方法，该方法可以通过证据深度学习来量化下水道缺陷预测的不确定性。同时，提出了一种新颖的专家基本利率分配（EBRA），以介绍在实际情况下描述可靠证据的专家知识。实验结果表明，在最新的公共基准上实现了TMSDC的有效性和不确定性估计的出色能力。
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.
