Visual detection of road cracks based on improved U-Net and morphological operations

Road cracks are a common road traffic safety problem. Methods such as manual measurement of cracks do not facilitate large-scale inspections, which affects the normal operation of roads and the safety of pedestrians and vehicles. In this paper, a visual measurement technique based on convolutional neural networks and morphological operations is proposed for automated and efficient detection of road cracks. This method adopts the U-Net network as the infrastructure, changes the backbone feature extraction network to the VGG-16 network and introduces multiple indicators to establish a new loss function to alleviate the sample imbalance problem. Finally, the crack features are combined to perform morphological operations on the image to enrich the recovered detail features. After experiments on the road crack dataset, this method has better crack segmentation capability and reliability compared to other algorithms.