Automating Bridge Inspection Procedures: Real-Time UAS-Based Detection and Tracking of Concrete Bridge Element

Abstract

Bridge inspections are necessary to maintain the safety, health, and welfare of the public. All bridges in the United States are federally mandated to undergo routine evaluations to confirm their structural integrity throughout their lifetime. The traditional process implements a bridge inspection team to conduct the inspection, heavily relying on visual measurements and subjective estimates of the existing state of the structure. Conducting unmanned automated bridge inspections would allow for a more efficient, accurate, and safer alternative to traditional bridge inspection procedures. Optimizing bridge inspections in this manner would enable frequent inspections in order to comprehensively monitor the health of bridges and quickly recognize minor problems which could be easily corrected before turning into more critical issues. In order to create an unmanned data acquisition procedure, unmanned aerial vehicles with high-resolution cameras will be employed to collect videos of the bridge under inspection. To automate a bridge inspection procedure employing machine learning methods, such as neural networks, and machine vision methods, such as Hough transform and Canny edge detection, will assist in identifying the entire beam. These methods along with future work in damage detection and assessment will be the main steps to create an unmanned automated bridge inspection.