Visual Measurement System for Wheel–Rail Lateral Position Evaluation

Skrickij, Viktor; Šabanovič, Eldar; Shi, Dachuan; Ricci, Stefano; Rizzetto, Luca; Бурейка, Гинтаутас

doi:10.3390/s21041297

Cited by 19 publications

(10 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can be applied for traffic objects detection and tracking, for vision-based localisation and navigation, to capture textures and colours. Usage of stereo and infrared cameras can expand their uses for geometric parameter estimation, capturing objects in dark lighting [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič¹,

Kojis²,

Šukevičius³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

With the automotive industry moving towards automated driving, sensing is becoming an increasingly important part of enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors for the estimation of vehicle unsprung mass relative velocity.

show abstract

Section: Introductionmentioning

confidence: 99%

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič¹,

Kojis²,

Šukevičius³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we mainly focus on the proposed algorithm for virtual point tracking. The calculation of displacement between the virtual points has been introduced and validated in [32]. The fusion of CV and accelerometers will be addressed in future work.…”

Section: Challenges and Contributionsmentioning

confidence: 99%

“…This paper focuses on virtual point tracking. The displacement calculation method has been introduced and validated in our previous work [32]. We define three reference points on the wheel (P w ) and rail (P r1 and P r2 ) respectively.…”

Section: Approach For Virtual Point Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Shi

Šabanovič

Rizzetto

et al. 2022

Mechanical Systems and Signal Processing

Self Cite

View full text Add to dashboard Cite

In the application of computer-vision-based displacement measurement, an optical target is usually required to prove the reference. If the optical target cannot be attached to the measuring objective, edge detection and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. We propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge to tackle this issue. Our approach consists of three steps: 1) automatic calibration for detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. The numerical experiments have been performed to evaluate our approach's performance and latency in a harsh railway environment with dynamic complex backgrounds. We make our code and data available at https://github. com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications.

show abstract

“…A visual measurement system for track gauge evaluation was developed, experimented, and presented in [ 9 ], involving setting of some measurement points, detection of those points and measurement of distance between them. The experiments were carried on Vilnius railway station yard.…”

Section: Related Workmentioning

confidence: 99%

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Minea

Dumitrescu

Dima

2021

Sensors

View full text Add to dashboard Cite

This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.

show abstract

Visual Measurement System for Wheel–Rail Lateral Position Evaluation

Cited by 19 publications

References 27 publications

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Contact Info

Product

Resources

About