An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model

Abstract

Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a dataset of pavement cracks and sealed cracks is created, which consists of 10,400 images obtained by a vehicle equipped with a highway condition monitor, with 202,840 labeled distress instances included in these pavement images. Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images. Compared with traditional annotation, the method we propose generates more object instances, and the localization is more accurate. Next, to achieve efficient crack detection, a semi-automatic crack annotation method is proposed, which reduces the working time by 80% compared with fully manual annotation. Finally, comparative experiments are conducted on our dataset using 13 currently prevailing object detection algorithms. The results show that dense and redundant annotation is effective; moreover, cracks and sealed cracks can be efficiently and accurately detected using the YOLOv5 series model and YOLOv5s is the most balanced model with an F1-score of 86.79% and an inference time of 14.8ms. The pavement crack and sealed crack dataset created in this study is publicly available.

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

“…Studies included for bibliometric analysis(305) Studies included for critical analysis (65) Figure 2. Overview of the literature retrieval and screening process.…”

“…Yang et al [65]. proposed a novel annotation methodology, a dense and redundant annotation method, with the objective of enhancing the efficiency of data collection and reducing the time required for data collection in the process of road crack recognition.…”

Deep Learning-Based Road Pavement Inspection by Integrating Visual Information and IMU

“…Some researchers chose to equip a vehicle platform with a standard camera to acquire pavement images from the vehicle's front view. Single-stage target detection algorithms YOLOv4-Tiny [31], Scaled-YOLOv4 [32], and YOLOv5 [30,33,34] were applied in pavement damage detection using road images captured from the front view of the vehicle, and they all achieved high accuracy. In summary, these studies demonstrated the effectiveness of using vehicle-mounted platforms with cameras to acquire road images and the application of deep learning approaches.…”