Description
Robots such as drones have been leveraged to perform structure health inspection such as bridge inspection. Big data of inspection videos can be collected by cameras mounted on drones. In this project, we develop image analysis algorithms to support bridge engineers to analyze the big video data. Bridge engineers define the region of interest initially, then the algorithm retrieves all related regions in the video, which facilitates the engineers to inspect the bridge rather than exhaustively check every frame of the video. To perform this task, we propose a Multi-scale Siamese Neural Network. The network is initially trained by one-shot learning and is fine-tuned iteratively with human in the loop. Our neural network is evaluated on three bridge inspection videos with promising performances.
Location
St. Louis, Missouri
Presentation Date
07 Aug 2019, 9:00 am - 9:20 am
Meeting Name
INSPIRE-UTC 2019 Annual Meeting
Department(s)
Computer Science
Second Department
Engineering Management and Systems Engineering
Third Department
Civil, Architectural and Environmental Engineering
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Source Publication Title
Proceedings of the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (2019: Aug. 4-7, St. Louis, MO)
Included in
Image Data Analytics to Support Engineers’ Decision-Making
St. Louis, Missouri
Robots such as drones have been leveraged to perform structure health inspection such as bridge inspection. Big data of inspection videos can be collected by cameras mounted on drones. In this project, we develop image analysis algorithms to support bridge engineers to analyze the big video data. Bridge engineers define the region of interest initially, then the algorithm retrieves all related regions in the video, which facilitates the engineers to inspect the bridge rather than exhaustively check every frame of the video. To perform this task, we propose a Multi-scale Siamese Neural Network. The network is initially trained by one-shot learning and is fine-tuned iteratively with human in the loop. Our neural network is evaluated on three bridge inspection videos with promising performances.
