# Automated Bridge Component Recognition using Video Data

arXiv: https://arxiv.org/abs/1806.06820 · HTML: https://mlsanity.com/paper/1806.06820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Image-Based Structural Analysis Using Computer Vision and LLMs: PhotoBeamSolver](https://mlsanity.com/paper/2603.21432/) (2026)

## Abstract (arXiv)

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.