Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number104745
Journal / PublicationAutomation in Construction
Online published9 Jan 2023
Publication statusPublished - Mar 2023


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.

Research Area(s)

  • Computer vision, Crack detection, Deep learning, Tiled sidewalk, Unmanned aerial vehicle, YOLO