Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles

Abstract

In this study, the three-phase missing region of interest area detection and damage localization methodology based on three-dimensional image coordinates was proposed. In Phase 1, the coordinate transformation is performed by the position and attitude information of the unmanned aerial vehicles and camera, and the coordinates of the center point of each acquired image are obtained with the distance information between the camera and the target surface. For Phase 2, the size of the field of view of every acquired image is calculated using the focal length and working distance of the camera. Finally, in Phase 3, the missing part of the region of interest area can be identified and any damage detected at the individual image level can also be localized on the whole inspection region using information about the sizes of the field of view in all images calculated in the previous phase. In order to demonstrate the proposed methodology, experimental validation was performed on the actual bridge pier and deck as well as the lab-scale concrete shear wall. In the tests, the missing area detection and damage localization results were compared with image stitching and human visual inspection results, respectively. Experimental validation results have shown that the proposed methodology identifies missing areas and damage locations within reasonable accuracy of 10 cm.

A novel approach to assess the seismic performance of deteriorated bridge structures by employing UAV‐based damage detection

“…For this reason, the need for additional UAV flight by calculating the acquired image coordinates and identifying the missing areas should be considered. In this study, the image coordinate estimation proposed by Yoon et al 24 was adopted to identify whether missing area is detected in ROI. In their study, they calculated the 3‐D image coordinates of the target bridge by employing the UAV and camera pose, GPS location, and distance measurements.…”

“…Jung et al 23 used UAV pose and GPS location, relative to the target structure, combined with camera pose, to develop accurate 3‐D maps of bridges for autonomous inspection and damage detection. Similarly, Yoon et al 24 used UAV and camera pose and distance to the target structure to develop a methodology to identify accurately the location and extent of structural damage. These results indicate that both UAV and camera pose, combined with the distance to the target structure, provide enhanced inspection and monitoring performance.…”

Vision-guided unmanned aerial system for rapid multiple-type damage detection and localization

“…40 Similar methods were also suggested, such as performing coordinate transformation by the position and attitude information of the UAS, 41 and the coordinates of each acquired image center can be obtained with the distance information between the camera and the target surface. [42][43] The other kind of method is to splice the images of the surface to obtain a panoramic view of the structural surface. The collected and identified images containing damage can also be directly displayed on the mosaic.…”

UAV-based inspection of bridge and tunnel structures: an application review

“…[131]- [136], [143], [150] Spalling [11], [62]- [65], [67], [81] [60], [61], [105], [106], [108], [115], [132], [134] Efflorescence [10], [11], [43], [45], [54], [58], [62]- [65], [67], [81] [60], [132] Exposed rebar [11], [62], [64], [65], [67], [70] [60], [108], [115], [150] Generic/others [10], [11], [54], [62]- [65], [67], [80], [81] [60], [108], [132], [150] Steel Corrosion [10], [11], [45], [54], [62]-…”