Pavement crack detection with side optimization and attention mechanism

Ensuring pavement quality and boosting the efficiency of road maintenance heavily rely on the capability to detect cracks automatically. Aiming at the shortcomings of existing methods in paying attention to crack features and the problem of easy loss of crack detail information in deep feature maps, this paper proposes a network model that integrates side optimization strategy and attention mechanism, using VGG16 as the backbone network. Firstly, to enhance the network's responsiveness to features of cracks, a lightweight shuffle attention module is incorporated following the backbone network's middle and high-level convolution layers. Secondly, in order to further enhance the capture ability of crack features, the corresponding attention module is embedded in the side output of each stage. Finally, the introduction of a spatial separable pyramid module, coupled with the creation of a residual attention fusion module, is aimed at refining the deep feature map to enhance the restoration of intricate crack details. The side network assisted in generating the final prediction image by fusing the different features at multiple levels. The model uses the weighted cross-entropy loss function to calculate the loss, and the trained network can accurately locate the crack in the complex background. To verify the validity of the proposed method, it was compared with six different methods on two public available datasets. The algorithm achieves a good result, and the <i>F-score</i> is 87.19%.