Abstract: Automatic crack detection is the key to ensure the quality of concrete pavement and improve the efficiency of road maintenance.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 attention mechanism and deep feature optimization strategy,using VGG-16 as the backbone network.Firstly,a lightweight shuffle attention mechanism is introduced after the middle and high level convolutions of the backbone network,aiming to improve the sensitivity of the network to crack features.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,a spatial separable pyramid module is proposed and an attention fusion module is designed to optimize the deep feature map and restore more crack details.The side network assisted in generating the final prediction image by fusing the low-level and high-level features at multiple levels.The network uses the binary cross-entropy loss function as the evaluation function,and the trained network model can accurately identify the crack position from the input original image under complex background.To verify the effectiveness of the proposed method,it is compared with six methods on three datasets,DeepCrack,CFD,and Crack500.The proposed algorithm shows excellent performance,and the F-score value reaches 87.19%.

Key words: Crack detection, Attention mechanism, Deep feature optimization, Multi-feature fusion, Shuffle attention, Spatial separable pyramid