Toward Purifying Defect Feature for Multilabel Sewer Defect Classification

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing a deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature extraction. However, the ambiguity of sewer defects in the feature space is ignored, deteriorating the performance of sewer inspection. There are two reasons for such ambiguity. First, the defect-irrelevant region interferes the feature extraction of the model. Second, the setting of multilabel is an inherent challenge of extracting discriminative feature for different defect classes from defect-relevant region. In this article, we propose a multilabel sewer defect classification method, which can purify the sewer defect components in latent spaces, thereby mitigating the ambiguity of sewer defects. Specifically, a novel self-purification module (SPM) is modeled to disentangle the ambiguity of sewer defect feature, which consists of intraclass purification (ICP) and interclass decorrelation (ICD). ICP utilizes the task-aware information to purify the sewer features, and ICD aims to eliminate the cross correlation and defect-irrelevant components simultaneously. Moreover, to ensure the reliability of SPM, center global alignment (CGA) is introduced to avoid the trivial solution. Experimental results demonstrate the superiority of the proposed method compared with seven state-of-the-art methods on the latest benchmark Sewer-ML. The proposed method outperforms the others approximately 8%
$\mathrm {F2_{\text {CIW}}}$
with a tolerant inference speed. Finally, the robustness of the proposed method is verified in two perturbed scenarios, where the reliable performance can be guaranteed against the limited perturbations.