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

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.

Automated summary

This paper proposes the integration of You Only Look Once (YOLO) into an unmanned aerial vehicle (UAV) for real-time crack detection in tiled sidewalks. Different network architectures of YOLOv2-tiny, Darknet19-based YOLOv2, ResNet50-based YOLOv2, YOLOv3, and YOLOv4-tiny are compared to improve accuracy and speed of detection. The results show that ResNet50-based YOLOv2 and YOLOv4-tiny offer excellent accuracy and speed, and remarkable ability in detecting small cracks. They also demonstrate good adaptability to environmental conditions such as shadows, rain, and motion-induced blurriness. The evaluation suggests the appropriate altitude and scanning area for the YOLO-UAV-based platform to achieve remote, reliable, and rapid crack detection.