Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicle

Abstract

In recent years, bridge collapses and fractures have occurred in various countries mostly following a lack of inspection and maintenance. External inspection processes can be very time-consuming and pose labor safety hazards. Terrain obstacles may also prevent the thorough inspection of some structures. The use of artificial intelligence instead of visual inspection by bridge inspectors is state-of-the-art. This study develops a Bayesian-optimized deep learning model for use on an unmanned aerial vehicle (UAV) to identify the deterioration patterns and segment areas of composite decks under bridges by computer vision-based techniques. The proposed module alters traditional labor-intensive methods of visual bridge inspections, reduces labor safety hazards, and increases inspection accuracy. It can be embedded in an artificial intelligence chip, which is then installed in a consumer-grade UAV, making it a dedicated drone for the external inspection of composite bridges.