  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.
