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

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.