RPD-YOLO: A Pavement Defect Dataset and Real-Time Detection Model

• Published in: IEEE access Vol. 12; pp. 159738 - 159747
• Main Authors: Tang, Hanqi, Zhou, Dandan, Zhai, Haozhou, Han, Yalu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Computational modeling; Datasets; Defect detection; Defects; Feature extraction; Highways; Image acquisition; Image edge detection; lightweight model; Neck; Object recognition; Pavement defect detection; Pavements; Real time; real-time object detection; Real-time systems; Roads; Safety; YOLO
• DOI: 10.1109/ACCESS.2024.3488522
• ISSN: 2169-3536

With a long-term usage, highways usually suffer from diverse pavement defects, which causes burdensome pavement defect detections on vast areas. To address this issue, the real-time and vehicle-mounted technique has been proposed and proved to be an efficient solution. However, as the detection systems are deployed on edge devices in complex environments, there exist several practical challenges in terms of real-time speed, detection performance and computational overhead. This study first provides a dataset captured by real-time and vehicle-mounted camera system. The height and angle where the high frame rate camera locates has been carefully designed to achieve the acquisitions of real-time images on highways. Then, this study proposes a novel lightweight network model named Real-time Pavement Detection of You Only Look Once (RPD-YOLO) with a lightweight Light C3 Ghost (LCG) block and an LCG Path Aggregation Network (LCG-PAN) neck structure, which can fully reduce the computational overheads and maintain a high precision and high speed during detection. Through a series of comparison experiments with current models, RPD-YOLO excels in overall balanced performance and can be deployed in resource-constrained devices to achieve real-time pavement defect detection.