Pavement Surface Defect Detection Using Mask Region-Based Convolutional Neural Networks and Transfer Learning

Pavement defect detection is critical for pavement maintenance and management. Meanwhile, the accurate and timely detection of pavement defects in complex backgrounds is a huge challenge for maintenance work. Therefore, this paper used a mask region-based convolutional neural network (Mask R-CNN) and transfer learning to detect pavement defects in complex backgrounds. Twelve hundred pavement images were collected, and a dataset containing corresponding instance labels of the defects was established. Based on this dataset, the performance of the Mask R-CNN was compared with faster region-based convolutional neural networks (Faster R-CNNs) under the transfer of six well-known backbone networks. The results confirmed that the classification accuracy of the two algorithms (Mask R-CNN and Faster R-CNN) was consistent and reached 100%; however, the average precision (AP) of the Mask R-CNN was higher than that of Faster R-CNNs. Meanwhile, the testing time of the models using a feature pyramid network (FPN) was lower than that of other models, which reached 0.21 s per frame (SPF). On this basis, the segmentation performance of the Mask R-CNN was further analyzed at three learning rates (LRs). The Mask R-CNN performed best with ResNet101 plus FPN as its backbone structure, and its AP reached 92.1%. The error rate of defect quantification was between 4% and 16%. It has an ideal detection effect on multi-object and multi-class defects on pavement surfaces, and the quantitative results of the defects can provide a reference for pavement maintenance personnel.