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In this paper, we introduce SHSnet, an advanced deep learning model designed for the efficient end-to-end segmentation of complex cracks, including thin, tortuous, and densely distributed ones. SHSnet features a non-uniform attention mechanism, a large receptive field, and boundary refinement to enhance segmentation performance while maintaining computational efficiency. To further optimize the model’s learning capability with highly imbalanced datasets, we employ a loss function (LP) based on the focal Tversky function. SHSnet shows very high performance, with values of 0.85, 0.83, 0.81, and 0.84 for precision, recall, intersection over union (IOU), and F-score, respectively. It achieves this with 10× fewer parameters than other models in the literature. Complementing SHSnet, we also present the post-processing unit (PPU), which analyzes crack morphological parameters through fracture mechanics and geometric properties. The PPU generates scanning lines to accurately compute these parameters, ensuring reliable results. The PPU shows a relative error of 0.4%, 1.2%, and 5.6% for crack number, length, and width, respectively. The methodology was benchmarked on complex ECC crack datasets as well as on multiple online datasets. In both of these cases, our results confirm that SHSnet consistently delivers superior performance and efficiency across various scenarios as compared to the methods in the literature.
Das et al. (Thu,) studied this question.