Abstract
Crack detection, classification, and characterization are key components of automatic structural health monitoring systems. Convolution-based encoder-decoder deep learning architecture has played a significant role in developing crack segmentation models possessing limitations in capturing the global context of the image. To overcome the stated limitation, in the present study, we propose a novel Self-Attention-based Efficient U-Net which effectively tries to solve this limitation. The proposed method achieved an F1 Score of 0.775, an IoU of 0.663, and an accuracy of 97.3% on the Crack500 dataset improving upon the current state-of-the-art models.
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Gupta, S., Shrivastwa, S., Kumar, S., Trivedi, A. (2023). Self-attention-Based Efficient U-Net for Crack Segmentation. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_9
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