Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion

Abstract

Crack detection for concrete pavement is an important fundamental task to ensure the safety of the road.Due to the complicated concrete pavement background and the diversity of cracks,a novel crack detection network of concrete pavement based on dilated convolution and multi-features fusion is proposed.The proposed network is based on the encoding-decoding structure of U-Net.In the encoding stage,the improved residual network Res2Net can be used to improve the ability of feature extraction.A cascade and parallel mode dilates convolution as center part,it can enlarge the receptive field of feature points,but without reducing the resolution of the feature maps.The decoder aggregates multi-scale and multi-level features from the low convolutional layers to the high-level convolutional layers,which improves the accuracy of crack detection.We use F-score to eva-luate our network performance.To demonstrate the validity and accuracy of the proposed method,we compare it with existing methods.The experiment results in multiple crack datasets reveal that our method is superior to these methods.The algorithm improves the accuracy of crack detection and has good robustness.