PDMS-FBG-Based Fiber Optic System for Traffic Monitoring in Urban Areas

Fajkus, Marcel; Fridrich, Michael; Nedoma, Jan; Kahánková, Radana; Martínek, Radek; Bednar, Emil; Kolařík, Jakub

doi:10.1109/access.2020.3006985

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…4) Asphalt road in Ostrava, Czech Republic: This project employs FBG sensors fixed into the road as well. Fajkus et al [29] chose to house the sensors in polydimethylsiloxane (PDMS), a polymeric two-component potting material due to its flexibility and strength. A 3-D printer was used for the potting process.…”

Section: B Road Pavementmentioning

confidence: 99%

Applications of fiber optic sensors in traffic monitoring: a review

Tekinay

Sylvester²,

Brunton³

et al. 2023

Innov. Infrastruct. Solut.

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Fiber optic sensors are becoming key tools in building more robust and accurate traffic monitoring systems. In this survey, we present various preliminary implementations and a discussion about some of the key design points. The scanning rates of the sensors should be at least 500 Hz to correctly capture the moving vehicles. The size of the sensors should not be larger than approximately 50 cm to enable individual axle detection. A minimum of 1µ sensor resolution is required to detect light weight vehicles. Adaptive filtering, Wavelet denoising and neural networks are some of the tools used to improve the performance. Sensors that reside on the surface of the pavement might lead to biased data and cause destructive implementation.There is yet to be a unified solution. We need a global, user-friendly, consistent, durable, environment-friendly solution with high system availability that would work on bridges, in urban areas and in tunnels. We believe precast slabs of road pavement embedded with sensors that are manufactured at a facility would give us the best opportunity to meet those demands.

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Section: B Road Pavementmentioning

confidence: 99%

Applications of fiber optic sensors in traffic monitoring: a review

Tekinay

Sylvester²,

Brunton³

et al. 2023

Innov. Infrastruct. Solut.

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“…Ground-based solutions can provide neat monitoring, management, and car classification. They are typically based on inductive and magnetic loops or may benefit from many sensing methods [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The sensing maps a measurable physical quantity into virtual data, creating a basis for computation, analytics, and adoption of artificial intelligence (AI) and machine learning (ML) algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…For a sensing part, anisotropic magneto-resistance [20], piezoelectricity [21,22], or hydro-electricity [23] are typically exploited. Alternative classes for on-road traffic control rely on embedded strain gauge [24], vibration [25], or fiber-optics [26][27][28][29][30][31][32][33][34][35][36] sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Coexistence between high-speed network and optical sensing platform is highly desired to keep both cost and complexity at low levels [36]. Fiber Bragg gratings (FBGs) emerge as an attractive optical platform to form single-and multi-point sensor arrays [26,27,[29][30][31][32][33][34][35]. In FBGs, the reflected light adds constructively in the backward direction and creates a distinct narrowband drop in the transmission, while the rest of the light passes through the grating.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural-Network-Optimized Vehicle Classification Using Clustered Image and Fiber-Sensor Datasets

Kamencay¹,

Benedikovič²,

Marković³

et al. 2023

IEEE Access

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Internet of Things (IoT) becomes indispensable for transport and automotive industry to advance functions in on-road traffic monitoring. Indeed, smart management tools and machine learning concepts are inevitable in vehicle categorization systems. However, existing systems are only built on individual platforms, and for a most part, the classification accuracy remains limited. In this work, these challenges are tackled by designing a novel convolutional neural network (CNN) that substantially improves on-road vehicle classification. In particular, we experimentally harness, to the best of our knowledge for the first time, two different datasets from separated technological platforms based on closecircuit television (CCTV) and fiber Bragg grating (FBG) sensors, respectively. Standard neural networks in single FBG platform yield limited classification accuracy of only 34% -62% for AlexNet, 51% -77% for GoogleNet, 57% -78% for ResNet-50, and 59% -86% for ResNet-101. In contrast, hybrid CNN classification with individual CCTV and FBG datasets substantially improves detection levels, reaching inclass accuracy up to 90% -97%. Moreover, this classification concept includes an intrinsic back-up verification with respect to each platform compensating the shortcomings of individual technologies. Our demonstration can make key advances towards near-unity accuracy in vehicle classifications for IoT systems, capitalizing on cost-effective and well-established platforms.

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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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PDMS-FBG-Based Fiber Optic System for Traffic Monitoring in Urban Areas

Cited by 6 publications

References 31 publications

Applications of fiber optic sensors in traffic monitoring: a review

Applications of fiber optic sensors in traffic monitoring: a review

Neural-Network-Optimized Vehicle Classification Using Clustered Image and Fiber-Sensor Datasets

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

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