Automatic Pavement Crack Identification Based on an Improved C-Mask Region-Based Convolutional Neural Network Model

Abstract

A pavement crack identification method based on an improved C-mask region-based convolutional neural network (R-CNN) model is proposed to solve problems whereby existing crack recognition algorithms exhibit low accuracy and cannot perform detection and segmentation tasks simultaneously. The crack dataset that was collected in this study included three categories: transverse cracks, longitudinal cracks, and alligator cracks. The model integrates the detection task and the segmentation task into one model, and segments the crack pixels in the generated detection box while achieving target positioning. Firstly, based on the mask R-CNN model, the improved C-mask R-CNN method is designed, which improves the quality of the region proposal box by combining the detectors that are cascaded with different intersections over union thresholds, and achieves accurate crack location under high-threshold detection. Secondly, the ratio of the anchor in the model is adjusted, and a series of optimization parameters and experimental comparisons are carried out for the improved model to realize the segmentation of the crack pixels in the generated detection box during the crack location. The effectiveness of the proposed model is verified, and finally, an evaluation method for cracks is proposed. Furthermore, the calculation of the crack geometric parameters is completed. The experimental results demonstrate that the mean average precision (mAP) of the C-mask R-CNN model detection part reached 95.4%, and the mAP of the segmentation part reached 93.5%. Moreover, the proposed model is convenient for researchers to deploy and implement.