Fusion segmentation method for pavement crack detection

In order to obtain the location information, distribution path, shape extension and density information of pavement cracks at the same time, the fusion of target detection algorithm and image segmentation algorithm is studied. After analyzing the network structure and feature fusion mode of target detection algorithm and image segmentation algorithm, a PSP-YOLO crack detection and segmentation algorithm based on YOLO V5 and PSPnet is proposed. At the same time, a data augmentation network based on GAN network is proposed to generate false fracture images to augment fracture samples. The experimental results show that the PSP-YOLO detection and segmentation algorithm can obtain the information of crack location and shape extension at the same time. The average accuracy of pavement crack detection under this data set is 93. 18%, and the average intersection over union of segmentation module is 74. 68%. Under the same experimental conditions, the average accuracy of the segmentation module is 2. 69% higher than that of the original YOLO V5, and the average intersection over union of the segmentation module is 1. 54% higher than that of the original PSP-net.