Abstract
Detecting cracks on the concrete surface is crucial for the tunnel health monitoring and maintenance of Chinese transport facilities, since it is closely related with the structural health and reliability. The automated and efficient tunnel crack detection recently has attracted more research studies, particularly cheap availability of digital cameras makes this issue easier. However, it is still a challenging task due to concrete blebs, stains, and illumination over the concrete surface. This paper presents an efficient crack detection method in the tunnel concrete structure based on digital image processing and deep learning. Three contributions of the paper are summarized as follows. First, we collect and annotate a tunnel crack dataset including three kinds of common cracks that might benefit the research in the field. Second, we propose a new coarse‐to‐fine crack detection method using improved preprocessing, coarse crack region localization and classification, and fine crack edge detection. Third, we introduce a faster region convolutional neural network to develop a coarse crack region localization and classification, then deploy edge extraction to implement the fine crack edge detection, gaining a high‐efficiency and high‐accuracy performance.This article is categorized under: Technologies > Machine Learning Application Areas > Industry Specific Applications Technologies > Classification
Published Version
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