Road Crack Detection Using Deep Neural Network Based on Attention Mechanism and Residual Structure

Intelligent detection of road cracks is crucial for road maintenance and safety. Due to the interference of illumination and different background factors, the road crack extraction results of existing deep learning methods are incomplete, and the extraction accuracy is low. We designed a new network model, called AR-UNet, which introduces a convolutional block attention module (CBAM) in the encoder and decoder of U-Net to effectively extract global and local detail information. The input and output CBAM features of the model are connected to increase the transmission path of features. The BasicBlock is adopted to replace the convolutional layer of the original network to avoid network degradation caused by gradient disappearance and network layer growth. We tested our method on DeepCrack, Crack Forest Dataset, and our own labeled road image dataset (RID). The experimental results show that our method focuses more on crack feature information and extracts cracks with higher integrity. The comparison with existing deep learning methods also demonstrates the effectiveness of our proposed method. The code is available at: https://github.com/18435398440/ARUnet.