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Abstract Efficient and precise identification of road pavement cracks contributes to better evaluation of road conditions. In practical road maintenance and safety assessment, traditional manual crack detection methods are time-consuming, physically demanding, and highly subjective. In addition, crack recognition based on image processing techniques lacks robustness. In this paper, a multi-branch feature fusion road crack segmentation network model (DTPC) based on deep convolution and transformer modules is proposed. The model is used for pixel-level segmentation of road crack images, which is a good solution to the existing needs and helps to repair dangerous cracks promptly in the follow-up work to prevent serious disasters due to crack breakage. Firstly, combine deep convolution with transformer modules to achieve precise local extraction and global contextual feature extraction. Secondly, a dual-channel attention mechanism is employed to help the model better address information loss and positional offset issues. Finally, three-branch outputs are fused to obtain prediction maps that intuitively determine recognition results. The proposed model is tested for accuracy using a dedicated road pavement crack dataset. Results show that compared to mainstream models such as SegFormer, HRNet, PSPNet, and fully convolutional network, the DTPC model achieves the highest MIoU score (86.72%) and F1 score (92.49%).
Lai et al. (Fri,) studied this question.