Fiber Optic Phase-Based Sensor for Detection of Axles and Wheels of Tram Vehicles

Nedoma, Jan; Kostelanský, Michal; Fridrich, Michael; Frnda, Jaroslav; Pinka, Miroslav; Martínek, Radek; Novák, Martin; Žabka, Stanislav

doi:10.26552/com.c.2020.3.119-127

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…In a recent scenario, vehicle type classification is the emerging research area in intelligent traffic systems, due to its wide range of applications that includes intelligent parking systems and traffic flow statistics [24]. Many approaches have been developed using vehicle type classification, which are commonly based on cameras, magnetic induction, and optic fibres [25]. The image-based approaches received great attention in the computer vision community with the extensive use of traffic surveillance cameras.…”

Section: Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

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In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

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Section: Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

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“…The curvature measurement is proposed using fiber-Bragg grating strain sensors. Similar applications of distributed fiber optic strain sensing for transport infrastructure are proposed in studies [ 13 ]. The paper [ 14 ] presented track-side monitoring of sleeper vertical deflection on a turnout.…”

Section: Introductionmentioning

confidence: 93%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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“…Some of them are also used in the field of transport for vehicle detection, for security monitoring of a structural load of road and motorway tunnels, etc. When we focus on physical principles based on optics, many of these sensors are based on interferometric measurements [5][6][7][8] and on the use of Bragg gratings [9][10][11][12]. Designs of compressive force and weight sensors that use the detection of changes in the intensity of optical power transmitted by an optical fiber are beginning to appear [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Measuring the pressure force by detecting the change in optical power intensity

Jargus

Kostelanský²,

Fridrich³

et al. 2022

Electro-Optical Remote Sensing XVI

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This article describes the research work in search of an optimized solution for the measurement of compressive force using the detection of the intensity of the optical power coupled into the optical fiber. In the experimental part of the research a product realized by 3D printing was used the outer case of which was made of FLEXFILL 98A material and the inner part was formed by a three-part PETG layer while the middle sensory part was changeable. This model was used to test different shapes of deformation elements in the variable part to find suitable configurations of the deformation plate. A standard 50/125 μm multimode graded index optical fiber was placed in the sensory part. It can be assumed that the results of this research can be used for the design of sensors based on the detection of changes in optical power intensity

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Fiber Optic Phase-Based Sensor for Detection of Axles and Wheels of Tram Vehicles

Cited by 3 publications

References 23 publications

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Measuring the pressure force by detecting the change in optical power intensity

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