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

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 syst...