ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-4/W3-2022
https://doi.org/10.5194/isprs-annals-X-4-W3-2022-57-2022
https://doi.org/10.5194/isprs-annals-X-4-W3-2022-57-2022
14 Oct 2022
 | 14 Oct 2022

AUTOMATIC DETECTION AND DIMENSIONAL MEASUREMENT OF MINOR CONCRETE CRACKS WITH CONVOLUTIONAL NEURAL NETWORK

Y. Guo, Z. Wang, X. Shen, K. Barati, and J. Linke

Keywords: Convolutional Neural Network, Semantic Segmentation, Digital Single-Lens Reflex Camera, Crack Detection, Crack Measurement

Abstract. The increasing number of aging infrastructures has drawn attention among the industry as the results caused by critical infrastructure failure could be destructive. It is essential to monitor the infrastructure assets and provide timely maintenance. However, one of the crucial problems is that the budget allocated to the maintenance stage is much less than that for the designing and construction stages. The cost of labor, equipment, and vehicles are significant. Therefore, it is impossible to perform a thorough inspection by human inspectors over each asset. A more efficient method will be needed to solve this problem. This paper aims to provide an automatic approach to detecting and measuring the dimensions of minor cracks that appear on concrete structures with a noisy background. This research also investigates the relationship between image pixel size, accuracy, detection rate of cracks, and shooting distance of images. The proposed method will be able to reduce the cost and increase accuracy. A case study was performed on a concrete sewer with cracks distributed on the surface in Sydney, New South Wales, Australia.