Intelligent detection of hidden distresses in asphalt pavement based on GPR and deep learning algorithm

Abstract

Hidden distresses of asphalt pavement are hidden dangers in the service of expressways. Efficient and accurate detection of hidden distresses is of great importance for maintaining and managing the expressways suffering from them. In this study, the intelligent detection technology of asphalt pavement hidden distresses was studied using the object detection algorithm and ground penetrating radar (GPR) image data. A GPR image data set was established to train and test the object detection model. And the distresses samples in the data set include settlement, hidden cracks and loosening. According to the distribution of hidden distresses in GPR image data, a two-way object detection model for targets of different scales was proposed, and each part of the two-way model was optimized and improved based on the basic model. At the same time, a copy-and-paste data enhancement scheme was designed to expand and balance the number of distress data samples. The final model greatly improved the detection accuracy of asphalt pavement hidden distresses. The results of the model test showed that the two-way object detection model proposed in this paper can effectively improve the detection accuracy of small-sized distress objects. Compared to the basic model, the final model had an increase of 17.9% in the average precision (AP) of small objects and a 9.9% increase in the comprehensive index AP (0.5: 0.95). The results of the ablation experiment show that the optimization methods for each part of the model improved the comprehensive detection effect of the model. When compared with other object detection models, the final model was superior to other models in all AP indicators, and had obvious advantages in the detection accuracy of the three types of hidden distresses. The confusion matrix of the test results of the final model showed that the recall rate of the final model for the three types of hidden distresses was above 96%. Thus, applying the final model established in this study to actual road detection can effectively improve the efficiency of distress detection in GPR images.