Multi-label Sewer Pipe Defect Recognition with Mask Attention Feature Enhancement and Label Correlation Learning
<|reference_start|>Multi-label 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 multi-label 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 dataset and exceeds the current best method by 11.87\% in terms of F2 metric on the full dataset, 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 which demonstrates a strong model interpretability. Our code is available at \href{https://github.com/shengyu27/MA-Q2L}{\textcolor{black}{https://github.com/shengyu27/MA-Q2L.}<|reference_end|>
@article{zuo2024multi-label,
    title={Multi-label Sewer Pipe Defect Recognition with Mask Attention Feature
  Enhancement and Label Correlation Learning},
    author={Xin Zuo, Yu Sheng, Jifeng Shen and Yongwei Shan},
    journal={arXiv preprint arXiv:2408.00489},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00489},
    primaryClass={cs.CV}
}