Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism

Abstract

As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method.

Keywords

• crack detection
• dynamic snake convolution
• attention mechanism
• CBAM
• U-Net network
• semantic segmentation