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Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images
Abstract The conventional method of manually verifying the quality of tiled sidewalks is laborious, because of the time-consuming identification of cracks from numerous grid-like elements of tiles. In this paper, the integration of You Only Look Once (YOLO) into an unmanned aerial vehicle (UAV) is proposed to achieve real-time crack detection in tiled sidewalks. Different network architectures of YOLOv2‑tiny, Darknet19-based YOLOv2, ResNet50-based YOLOv2, YOLOv3, and YOLOv4‑tiny are reframed and compared to get better accuracy and speed of detection. The results show that ResNet50-based YOLOv2 and YOLOv4‑tiny offer excellent accuracy (94.54% and 91.74%, respectively), fast speed (71.71 fps and 108.93 fps, respectively), and remarkable ability in detecting small cracks. Besides, they demonstrate excellent adaptability to environmental conditions such as shadows, rain, and motion-induced blurriness. From the assessment, the appropriate altitude and scanning area for the YOLO-UAV-based platform are suggested to achieve remote, reliable, and rapid crack detection.
Graphical abstract Display Omitted
Highlights You Only Look Once (YOLO) is employed for crack detection of tiled sidewalks. Network structures in YOLO are reframed and compared, for accuracy-speed tradeoff. ResNet50-based YOLOv2 and YOLOv4‑tiny are recommended in YOLO-UAV-based platform. Effects of shadow, darkness, plant twig, rainwater, and blur are clarified. YOLO-UAV-based platform for real-time inspection of tiled sidewalks is assessed.
Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images
Abstract The conventional method of manually verifying the quality of tiled sidewalks is laborious, because of the time-consuming identification of cracks from numerous grid-like elements of tiles. In this paper, the integration of You Only Look Once (YOLO) into an unmanned aerial vehicle (UAV) is proposed to achieve real-time crack detection in tiled sidewalks. Different network architectures of YOLOv2‑tiny, Darknet19-based YOLOv2, ResNet50-based YOLOv2, YOLOv3, and YOLOv4‑tiny are reframed and compared to get better accuracy and speed of detection. The results show that ResNet50-based YOLOv2 and YOLOv4‑tiny offer excellent accuracy (94.54% and 91.74%, respectively), fast speed (71.71 fps and 108.93 fps, respectively), and remarkable ability in detecting small cracks. Besides, they demonstrate excellent adaptability to environmental conditions such as shadows, rain, and motion-induced blurriness. From the assessment, the appropriate altitude and scanning area for the YOLO-UAV-based platform are suggested to achieve remote, reliable, and rapid crack detection.
Graphical abstract Display Omitted
Highlights You Only Look Once (YOLO) is employed for crack detection of tiled sidewalks. Network structures in YOLO are reframed and compared, for accuracy-speed tradeoff. ResNet50-based YOLOv2 and YOLOv4‑tiny are recommended in YOLO-UAV-based platform. Effects of shadow, darkness, plant twig, rainwater, and blur are clarified. YOLO-UAV-based platform for real-time inspection of tiled sidewalks is assessed.
Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images
Qiu, Qiwen (author) / Lau, Denvid (author)
2023-01-04
Article (Journal)
Electronic Resource
English
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