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There have been great advances in bridge inspection damage detection involving the use of deep learning models. However, automated detection models currently fall short of giving an inspector an understanding of how the damage has progressed from one inspection to the next. The rate-of-change of the damage is a critical piece of information used by engineers to determine appropriate maintenance and rehabilitation actions to prevent structural failures. We propose a simple methodology for registering two bridge inspection videos or still images, collected at different stages of deterioration, so that trained model predictions may be directly measured and damage progression compared. The changes may be documented and presented to the inspector so that they may quickly evaluate key interest regions in the inspection video or image. Three approaches referred to as rigid, deformable, and hybrid image registration methods were experimentally tested and evaluated based on their ability to preserve the geometric characteristics of the referenced image. It was found in all experiments that the rigid, homography-based transformations performed the best for this application over a state-of-the-art deformable registration method, RANSAC-Flow.

 

The use of augmented reality (AR) with drones in infrastructure inspection can increase human capabilities by helping workers access hard-to-reach areas and supplementing their field of view with useful information. Still unknown though is how these aids impact performance when they are imperfect. A total of 28 participants flew as an autonomous drone while completing a target detection task around a simulated bridge. Results indicated significant differences between cued and un-cued trials but not between the four cue types: none, bounding box, corner-bound box, and outline. Differences in trust amongst the four cues indicate that participants may trust some cue styles more than others. 

The use of augmented reality (AR) with drones in infrastructure inspection can increase human capabilities by helping workers access hard-to-reach areas and supplementing their field of view with useful information. Still unknown though is how these aids impact performance when they are imperfect. A total of 28 participants flew as an autonomous drone while completing a target detection task around a simulated bridge. Results indicated significant differences between cued and un-cued trials but not between the four cue types: none, bounding box, corner-bound box, and outline. Differences in trust amongst the four cues indicate that participants may trust some cue styles more than others.