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

Abstract

Intelligent detection of road cracks is crucial for road maintenance and safety. because of the interference of illumination and totally different background factors, the road crack extraction results of existing deep learning ways square measure incomplete, and therefore the extraction accuracy is low. we tend to designed a brand new network model, referred to as AR-UNet, that introduces a convolutional block attention module (CBAM) within 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 tagged road image dataset (RID). The experimental results show that our method focuses additional on crack feature info and extracts cracks with higher integrity. The comparison with existing deep learning ways conjointly demonstrates the effectiveness of our projected technique. The code is out there at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/18435398440/ARUnet</uri> .