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Crack-Att Net: crack detection based on improved U-Net with parallel attention

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Abstract

In recent years, the demand for automatic crack detection has increased rapidly. Due to the particularity of crack images, that is, the proportion of cracks in the entire images is very small, and some cracks in the image are particularly slender and light, it brings challenge for automatic crack detection. In this paper, we propose an end-to-end pixel-level crack segmentation network, named as “Crack-Att Net”. In our approach, firstly, an encoder network is used to extract the crack features; then, crack features generated by the encoder and decoder networks at the same scale are pairwisely fused through a parallel attention mechanism added for accurately locating the cracks; finally, the fused crack feature maps at all scales are further fused into a multi-scale feature-fusion map for crack detection. Experiments results on three existing datasets and an augmented dataset show that our proposed Crack-Att Net outperforms the current state-of-the-art methods.

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Data availability

Based on existing available datasets from the CFD datasets [35], CrackTree 200 [46], DeepCrack [20] and crack images we have collected, we have composed the Crack Segmentation Dataset 1121(or called CSD1121).

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Acknowledgements

This research was funded by the National Key Research and Development Program of China (No. 2021YFC2902000). Thanks are given to the anonymous reviewers for their careful reviews and detailed comments.

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Authors and Affiliations

  1. College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China

    Na Xu & Lizhi He

  2. Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, Hong Kong, China

    Qing Li

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  1. Na Xu
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Xu, N., He, L. & Li, Q. Crack-Att Net: crack detection based on improved U-Net with parallel attention. Multimed Tools Appl 82, 42465–42484 (2023). https://doi.org/10.1007/s11042-023-15201-7

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