Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Surface cracks of civil infrastructure are one of the important indicators for structural durability and integrity. Concrete cracks are typically investigated by manual visual observation on the surface, which is intrinsically subjective because it highly depends on the experience of inspectors. Furthermore, manual visual inspection is time-consuming, expensive, and often unsafe when inaccessible structural components need to be assessed. Computer vision-based approach is recognized as a promising alternative that can automatically extract crack information from images captured by the digital camera. As texts and cracks are similar in terms of consisting distinguishable lines and curves, image binarization developed for text detection can be appropriate for crack identification purposes. However, although image binarization is useful to separate cracks and backgrounds, the crack assessment is difficult to standardize owing to the high dependence of binarization parameters determined by users. Another critical challenge in digital image processing for crack detection is to automatically distinguish cracks from an image containing actual cracks and crack-like noise patterns (e.g., stains, holes, dark shadows, and lumps), which are often seen on the surface of concrete structures. In addition, a tailored camera system and the corresponding strategy are necessary to effectively address the practical issues in terms of the skewed angle and the process of the sequential crack images for efficient measurement. This research develops a computer vision-based approach in conjunction with deep learning for accurate crack evaluation of for civil infrastructure. The main contribution of the proposed approach can be summarized as follows: (1) a deep learning-based approach for crack detection, (2) a hybrid image processing for crack quantification, and (3) camera systems for the practical issues on civil infrastructure in terms of a skewed angle problem and an efficient measurement with the sequential crack images. The proposed research allows accurate crack evaluation to provide a proper maintenance strategy for civil infrastructure in practice.clos
