Weakly supervised collaborative localization learning method for sewer pipe defect detection

Abstract

Long-term corrosion and external disturbances can lead to defects in sewer pipes, which threaten important parts of urban infrastructure. The automatic defect detection algorithm based on closed-circuit televisions (CCTV) has gradually matured using supervised deep learning. However, there are different types and sizes of sewer pipe defects, and relying on human inspection to detect defects is time-consuming and subjective. Therefore, a few-shot, accurate and automatic method for sewer pipe defect with localization and fine-grained classification is needed. Thus, this study constructs a few-shot image-level dataset of 15 categories using the sewer dataset ML-Sewer and then presents a collaborative localization network based on weakly supervised learning to automatically classify and detect defects. Specifically, an attention refinement module (ARM) is designed to obtain classification results and high-level semantic features. Furthermore, considering the correlation between target regions and the extraction of target edge information, we designed a collaborative localization module (CLM) consisting of two branches. Then, to ensure that the network focuses on the complete target area, this study applies an image iteration module (IIM). Finally, the results of the two branches in the CLM are fused to acquire target localization. The experimental results show that the proposed model exhibits favorable performance in detecting sewer pipe defects. The proposed method exhibits prediction classification accuracy that reaches 69.76\(\%\) and a positioning accuracy rate that reaches 65.32\(\%\), which is higher than the performances of other weakly supervised detection models in sewer pipe defect detection.