# Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

arXiv: https://arxiv.org/abs/2207.03758 · HTML: https://mlsanity.com/paper/2207.03758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)

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

- [Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data](https://mlsanity.com/paper/2309.01574/) (2023)

## Abstract (arXiv)

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is in most cases a prerequisite. To avoid the use of conventional axle detectors and bridge type specific methods, we propose a novel method for axle detection through the placement of accelerometers at any point of a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This enables our method to use acceleration signals at any location of the bridge structure serving as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Our results on the measurement data show that our model detects 95% of the axes, thus, 128,599 of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles can be detected with a maximum spatial error of 20cm, with a maximum velocity of vmax=56,3~m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.