Imaging-based crack detection on concrete surfaces using You Only Look Once network

Abstract

The detection of cracks in concrete structures is a pivotal aspect in assessing structural robustness. Current inspection methods are subjective, relying on the inspector’s experience and mental focus. In this study, an ad hoc You Only Look Once version 2 object detector was applied to automatically detect concrete cracks from real-world images, which were taken from diverse concrete bridges and contaminated with handwriting scripts. A total of 3010 cropped images were used to generate the dataset, labelled for two different detection classes, that is, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Three full-scale images that contained disturbing background information were used to evaluate the robustness of the trained detector. The influence of labelling handwriting as an object class for network training on the overall crack detection accuracy was assessed as well. The results of this study show that the You Only Look Once version 2 could automatically locate crack with bounding boxes from raw images, even with the presence of handwriting scripts. As a comparative study, the proposed network was also compared with faster region-based convolutional neural network. The results showed that You Only Look Once version 2 performed better in terms of both accuracy and inference speed.