Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm

Detecting concealed cracks in asphalt pavement has been a challenging task due to the nonvisibility of the location of these cracks. This study proposes an effective method to automatically perform the recognition and location of concealed cracks based on 3-D ground penetrating radar (GPR) and deep learning models. Using a 3-D GPR and a filtering process, a dataset was constructed, including 303 GPR images and 1306 cracks. Next, You Only Look Once (YOLO) models were first introduced as deep learning models for detecting concealed cracks using GPR data. The results reveal that this proposed method is feasible for the detection of concealed cracks. Compared with YOLO version 3, YOLO version 4 (YOLOv4) and YOLO version 5 (YOLOv5) both achieve obvious progress even in a small dataset. The fastest detection speed of YOLOv4 models reaches 10.16 frames per second using only a medium CPU and the best mAP of YOLOv5 models is up to 94.39%. In addition, the YOLOv4 models show better robustness than the YOLOv5 models and could accurately distinguish between concealed cracks and pseudo cracks. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces an effective method utilizing 3-D ground penetrating radar (GPR) and deep learning models to automatically detect concealed cracks in asphalt pavement. The results demonstrate the feasibility of the proposed method, with YOLOv4 and YOLOv5 models showing significant advancements in crack detection even with a small dataset.