Automated bridge component recognition using video data
This paper investigates the automated recognition of structural bridge components using video data.Although understanding video data for structural inspections is straightforward for human inspectors,the implementation of the same task using machine learning methods has not been fully realized.In particular,single-frame image processing techniques,such as convolutional neural networks(CNNs),are not expected to identify structural components accurately when the image is a close-up view,lacking contextual information regarding where on the structure the image originates.Inspired by the significant progress in video processing techniques,this study investigates automated bridge component recognition using video data,where the information from the past frames is used to augment the understanding of the current frame.A new simulated video dataset is created to train the machine learning algorithms.Then,convolutional Neural Networks(CNNs)with recurrent architectures are designed and applied to implement the automated bridge component recognition task.Results are presented for simulated video data,as well as video collected in the field.
Bridge component recognition Video data Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM)
Yasutaka Narazaki Vedhus Hoskere Tu A.Hoang Billie F.Spencer
Department of Civil and Environmental Engineering,University of Illinois at Urbana-Champaign,Urbana-Champaign,USA
国际会议
The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)
青岛
英文
1630-1639
2018-07-22（万方平台首次上网日期，不代表论文的发表时间）