Abstract
Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.
Highlights
The quality parameters for structural layers of pavement are obtained through reasonable setpoint, drilled core on-site and laboratory testing in core sample detection
In the present study, we developed a method for evaluating the maintenance benefits by comparing the traditional detection and ground-penetrating radar (GPR) detection in asphalt pavement
This work proposes a method for combining the you only look once (YOLO) series with GPR images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits
Summary
The quality parameters for structural layers of pavement are obtained through reasonable setpoint, drilled core on-site and laboratory testing in core sample detection. As for image recognition of GPR detection, many researchers have tried to apply the complex manual processes to automatically inspect internal defects in a road, but this goal is difficult to realize [11,12]. It was not until the appearance of deep learning (DL), the real, efficient, automatic detection of concealed defects became possible in asphalt pavement [13,14]. This work proposes a method for combining the YOLO series with GPR images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
