Computer-Vision Based UAV Inspection for Steel Bridge Connections

Corrosion on steel bridge members is one of the most important bridge deficiencies that must be carefully monitored by inspectors. Human visual inspection is typically conducted first, and additional measures such as tapping bolts and measuring section losses can be used to assess the level of corrosion. This process becomes a challenge when some of the connections are placed in a location where inspectors have to climb up or down the steel members. To assist this inspection process, we developed a computervision based Unmanned Aerial Vehicle (UAV) system for monitoring the health of critical steel bridge connections (bolts, rivets, and pins). We used a UAV to collect images from a steel truss bridge. Then we fed the collected datasets into an instance level segmentation model using a region-based convolutional neural network to train characteristics of corrosion shown at steel connections with sets of labeled image data. The segmentation model identified locations of the connections in images and efficiently detected the members with corrosion on them. We evaluated the model based on how precisely it can detect rivets, bolts, pins, and corrosion damage on these members. The results showed robustness and practicality of our system which can also provide useful health information to bridge owners for future maintenance. These collected image data can be used to quantitatively track temporal changes and to monitor progression of damage in aging steel structures. Furthermore, the system can also assist inspectors in making decisions for further detailed inspections.