Abstract: Aiming at the pavement crack detection problem under complex background,due to the unsatisfactory detection effect of image segmentation algorithm based on deep learning,and the imbalance of pixel categories in the crack image itself,this paper proposes a pavement crack detection network based on attention mechanism and deformable convolution,which is constructed based on encoder-decoder structure.In order to solve the problem of difficult crack detection in complex background,firstly,deformable convolutional is used to improve the learning ability of linear features of cracks with different shapes.Secondly,the dense connection mechanism is used to strengthen the feature information.Then,in the decoder stage,the feature fusion of transpose convolution and bridge are adopted,and the multi-stage feature fusion is combined to improve the detection accuracy of the network.Finally,the attention module(SimAM) is introduced to pay more attention to the extraction of target features and suppress background features without increasing network parameters.Experiments are carried out on two open crack datasets to ve-rify the effectiveness of the algorithm.The experimental results show that the performance evaluation criteria of the algorithm are better than the comparison algorithms.The mean pixel accuracy and mean intersection over union of the BCrack dataset reached 92.12% and 84.79%,respectively.The mean pixel accuracy and mean intersection over union of the CFD dataset reached 91.02% and 74.75%,respectively.The average accuracy and average intersection ratio of CFD data set is 91.02% and 74.75%,respectively.The algorithm performs well in crack detection under complex background,and can be applied to pavement maintenance engineering.

Key words: Crack detection, Encoder-decoder structure, Deformable convolutional, Dense connection mechanism, Attention module