Multilabel Sewer Pipe Defect Recognition with Mask Attention Feature Enhancement and Label Correlation Learning

Xin Zuo, Yu Sheng, Jifeng Shen, Yongwei Shan

Abstract: The coexistence of multiple defect categories as well as the substantial class imbalance problem significantly impair the detection of sewer pipeline defects. To solve this problem, a multilabel pipe defect recognition method is proposed based on mask attention-guided feature enhancement and label correlation learning. The proposed method can achieve current approximate state-of-the-art classification performance using just 1/16 of the Sewer-ML training data set and exceeds the current best method by 11.87% in terms of F2 metric on the full data set, while also proving the superiority of the model. The major contribution of this study is the development of a more efficient model for identifying and locating multiple defects in sewer pipe images for a more accurate sewer pipeline condition assessment. Moreover, by employing class activation maps, our method can accurately pinpoint multiple defect categories in the image, demonstrating strong model interpretability.

External IDs:dblp:journals/jccee/ZuoSSS25