Vision-based autonomous bolt-looseness detection method for splice connections: Design, lab-scale evaluation, and field application

• Published in: Automation in construction 2021-04, Vol.124, p.103591, Article 103591
• Main Author: Huynh, Thanh-Canh
• Format: Article
• Language: English
• Subjects: Adaptive Canny method; Artificial neural networks; Automatic detection; Bolt-looseness; Bolted joints; Dragon Bridge; Faster RCNN; Loosening; Splice connection; Vision; Vision-based method
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2021.103591

This study presents a novel autonomous vision-based bolt-looseness detection method for splice bolted connections. The method is sequentially designed with a Faster regional convolutional neural network-based bolt detector, an automatic distortion corrector, an adaptive bolt-angle estimator, and a bolt-looseness classifier. The robustness of the method is demonstrated by detecting loosened bolts in a lab-scale bolted joint under sharp capturing angles and different lighting conditions. Next, the method is applied to detect loosened bolts in a realistic joint of the Dragon Bridge in Danang, Vietnam. The bolt detector shows the training, validation, and testing accuracy of 98.85%, 97.48%, and 93%, respectively. Loosened bolts in the lab-scale and real-scale joints are well detected with precisely-estimated loosening severities, even for a sharp perspective angle. The method also shows a high level of adaptability with low-brightness images. Therefore, the method has great potentials for autonomous monitoring of in-situ bolted connections. •A newly-developed vision-based autonomous method for bolt-looseness assessment•Fast, accurate, and automatic bolt detection using Faster RCNN•Automatic perspective correction and adaptive bolt-angle estimation•Good performance to different lighting conditions and sharp capturing angles•Potentials for assessing real-scale bolted joints