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- W4313854927 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." @default.
- W4313854927 created "2023-01-10" @default.
- W4313854927 creator A5038786437 @default.
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- W4313854927 date "2023-03-01" @default.
- W4313854927 modified "2023-10-12" @default.
- W4313854927 title "Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images" @default.
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- W4313854927 doi "https://doi.org/10.1016/j.autcon.2023.104745" @default.
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