Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle

Abstract

Information on the location of cracks in concrete structures is an important factor enabling appropriate maintenance or reinforcement measures to be taken. Most studies related to concrete cracks are limited to crack detection and identification, and studies related to crack location information are insufficient. The novelty of this study is to develop application technology related to crack localization by proposing a methodology that can estimate the location of concrete cracks even when reference objects or feature points are lacking using an unmanned aerial vehicle and image processing techniques. For the development and verification of the proposed method, aerial photography and image acquisition were performed using mounting a laser pointer model on an unmanned aerial vehicle. To build the analysis data, image distortion correction and feature point extraction were performed using the homography matrix and scale-invariant feature transform algorithm. Spatial information was established using the point cloud technique and image stitching technique, and crack localization was estimated using generating crack expression data via layer merging. The proposed method was validated using comparison with field-measured data. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Numerical simulation of the defect's behavior in building structure elements during ultrasonic diagnostics

“…Articles [12,13] develops a method for determining the criticality of cracks in concrete products. This method uses a neural network model YOLOv4 to identify concrete cracks.…”

A Large-Crack Image-Stitching Method with Cracks as the Regions of Interest

“…For such cross-image crack damage analysis, image stitching can be employed to construct complete crack images for assessment [6,7]. Traditionally, the most commonly used method is to utilize the scale-invariant feature transform (SIFT) for image stitching [8][9][10]. However, while the SIFT excels in maintaining scale invariance, its drawbacks include susceptibility to the erroneous matching of keypoints, resulting in suboptimal stitching results [10]; it also involves a considerable computational burden, particularly for highresolution images, to the extent that it may be impractical [9].…”