Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle

Abstract

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

A Multispectral Ariel Image Stitching using Decortification and EEG Signal Extraction Technique

“…Woo et al [16] proposed to drastically lower the error level, identification of fissures was determined using comparative position between components in drone-captured photos rather than using absolute position information. A total of 97 photos were collected using aerial photography.…”

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

“…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]. For the issue of erroneous matching, Tian et al [10] utilized the geometric relationships between crack feature points to improve matching accuracy, while Woo et al [11] used the SIFT combined with the Random Sample Consensus (RANSAC) algorithm [12] to compute homography matrices for image stitching. Additionally, researchers have explored various feature-matching methods combined with RANSAC for crack image-stitching tasks; Da et al [13] and Wu et al [14], for instance, used the SURF and ORB (Oriented FAST and Rotated BRIEF) algorithms, respectively, in combination with RANSAC.…”

A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles

“…Apart from the perspectives of crack information extraction, Yu et al [ 16 ] developed a fast feature-based stitching algorithm to detect cracks on the large panorama using an off-the-shelf DJI UAV for concrete bridge monitoring. Towards localizing cracks in concrete structures using a UAV, Woo et al [ 17 ] proposed a method utilizing relative positions between reference objects in UAV-captured images and revealed errors in the range of 24–84 mm and 8–48 mm on the x- and y- directions. In their study, the size of the reference object was first estimated by a point-cloud-based method, and the unit pixel size was then obtained to estimate the relative positions of the cracks using the point-cloud technique, image stitching, and homography matrix algorithms.…”