A method of hybrid dilated and global convolution networks for pavement crack detection

Abstract

Automatic crack detection is important for efficient and economical pavement maintenance. With the development of Convolutional Neural Networks (CNNs), crack detection methods have been mostly based on CNNs. In this paper, we propose a novel automatic crack detection network architecture, named hybrid dilated and global convolutional networks. Firstly, we integrate the hybrid dilated convolution module into ResNet-152 network, which can effectively aggregate global features. Then, we use the global convolution module to enhance the classification and localization ability of the extracted features. Finally, the feature fusion module is introduced to fuse multi-scale and multi-level feature maps. The proposed network can capture crack features from a global perspective and generate the corresponding feature maps. In order to demonstrate the effectiveness of our proposed method, we evaluate it on the four public crack datasets, DeepCrack, CFD, Cracktree200 and CRACK500, which achieves ODS values as 87.12%, 83.96%, 82.66%, 81.35% and OIS values as 87.55%, 84.82%, 83.56% and 82.98%. Compared with HED, RCF, DeepCrackT, FPHBN, ResNet-152 and DeepCrack, the ODS value performance improvement made in our method is 1.21%, 3.35%, 3.07%, 3.36%, 4.79% and 1% on DeepCrack dataset. Sufficient experimental statistics certificate that our proposed method outperforms other state-of-the-art crack detection, edge detection and image segmentation methods.