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Author (up) Carrasco, M.; Araya-Letelier, G.; Velazquez, R.; Visconti, P. doi  openurl
  Title Image-Based Automated Width Measurement of Surface Cracking Type
  Year 2021 Publication Sensors Abbreviated Journal Sensors  
  Volume 21 Issue 22 Pages 7534  
  Keywords surface cracks; crack characterization; infrastructure durability assessment  
  Abstract The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.  
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  Series Volume Series Issue Edition  
  ISSN 1424-8220 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000726832500001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1509  
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