YOLO Based Crack Detection of Structures with Edge Detection Process

In this paper, we propose an algorithm that combines the YOLO model with edge detection techniques to effectively detect cracks occurring in structures. Existing manual inspection methods are time-consuming, costly, and suffer from poor accuracy. To solve this problem, this study proposes an automated deep learning-based detection method, specifically using a YOLO model and a canny edge detection algorithm to perform more precise crack boundary extraction. The proposed algorithm consists of two steps. The first step is image preprocessing, which uses several preprocessing techniques such as gray-scale conversion, median blur, normalization, and brightness inversion to highlight the edges of cracks. In the second step, the canny edge detection algorithm is used to detect the boundaries of the cracks in the preprocessed image, and the YOLOv8 model is used to learn and detect the location of the corresponding cracks. The results show that the best performance is achieved when the YOLO model is trained using 6 steps of preprocessing and canny edge detection.