Research on Automatic Pavement Crack Recognition Based on the Mask R-CNN Model
Pengcheng Wang, Chao Wang, Hongwu Liu, Ming Liang, Wenhui Zheng, Hao Wang, Shichao Zhu, Guoqiang Zhong, Shang Liu
Abstract
Pavement will inevitably be damaged in the process of use; pavement damage detection and assessment are an important part of maintenance management. In order to prevent road diseases, it is necessary to fix the road cracks and implement automatic road crack inspection and monitoring. In this paper, the automatic identification of road cracks is realized by constructing the Mask R-CNN model. The labeled area can be segmented by pixels and positioned at the original data by integrating the image data used for training and the labeled data into a network model. The effect of the training model can be improved by increasing the number of data sets, the pixel of the fracture image, the background of the fracture, and the marking method of the fracture type. The validity and accuracy of the test results were characterized by RPN bounding-box loss, classification loss, mask loss, and total loss.