{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:10:24Z","timestamp":1776442224050,"version":"3.51.2"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005357","name":"Agent\u00fara na Podporu V\u00fdskumu a V\u00fdvoja","doi-asserted-by":"publisher","award":["APVV-17-0631"],"award-info":[{"award-number":["APVV-17-0631"]}],"id":[{"id":"10.13039\/501100005357","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006109","name":"Vedeck\u00e1 Grantov\u00e1 Agent\u00fara M\u0160VVa\u0160 SR a SAV","doi-asserted-by":"publisher","award":["VEGA 1\/0840\/18"],"award-info":[{"award-number":["VEGA 1\/0840\/18"]}],"id":[{"id":"10.13039\/501100006109","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministerstvo \u0161kolstva, vedy v\u00fdskumu a \u0161portu Slovenskej republiky","award":["ITMS 26210120021"],"award-info":[{"award-number":["ITMS 26210120021"]}]},{"name":"Ministerstvo \u0161kolstva, vedy v\u00fdskumu a \u0161portu Slovenskej republiky","award":["ITMS 26220220183"],"award-info":[{"award-number":["ITMS 26220220183"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.<\/jats:p>","DOI":"10.3390\/s20164472","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T09:04:16Z","timestamp":1597050256000},"page":"4472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Vehicle Classification Based on FBG Sensor Arrays Using Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-5182","authenticated-orcid":false,"given":"Michal","family":"Frniak","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1968-1478","authenticated-orcid":false,"given":"Miroslav","family":"Markovic","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4875-973X","authenticated-orcid":false,"given":"Patrik","family":"Kamencay","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1201-1474","authenticated-orcid":false,"given":"Jozef","family":"Dubovan","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miroslav","family":"Benco","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milan","family":"Dado","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/TITS.2014.2333003","article-title":"Tracking Heavy Vehicles Based on Weigh-In-Motion and Inductive Loop Signature Technologies","volume":"16","author":"Jeng","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Santoso, B., Yang, B., Ong, C.L., and Yuan, Z. (2018, January 23\u201327). Traffic Flow and Vehicle Speed Measurements using Anisotropic Magnetoresistive (AMR) Sensors. Proceedings of the 2018 IEEE International Magnetics Conference (INTERMAG), Singapore.","DOI":"10.1109\/INTMAG.2018.8508869"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Xu, C., Wang, Y., Bao, X., and Li, F. (2018). Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor. Sensors, 18.","DOI":"10.3390\/s18061690"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27201","DOI":"10.3390\/s151027201","article-title":"Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors","volume":"15","author":"Lamas","year":"2015","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, H., and Wang, Y. (November, January 30). Simulation of piezoelectric sensor in weigh-in-motion systems. Proceedings of the 2015 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications (SPAWDA), Jinan, China.","DOI":"10.1109\/SPAWDA.2015.7364457"},{"key":"ref_6","unstructured":"(2020, August 10). Weigh-in-Motion Pocket Guide, Available online: https:\/\/www.fhwa.dot.gov\/policyinformation\/knowledgecenter\/wim_guide\/."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012060","DOI":"10.1088\/1757-899X\/147\/1\/012060","article-title":"Researches regarding a pressure pulse generator as a segment of model for a weighing in motion system","volume":"147","author":"Mardare","year":"2016","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-Tarawneh, M., Huang, Y., Lu, P., and Bridgelall, R. (2019). Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept. IEEE Trans. Intell. Transp. Syst., 1\u201312.","DOI":"10.1109\/TITS.2019.2949242"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bajwa, R., Coleri, E., Rajagopal, R., Varaiya, P., and Flores, C. (2017). Development of a Cost Effective Wireless Vibration Weigh-In-Motion System to Estimate Axle Weights of Trucks. Comput.-Aided Civ. Infrastruct. Eng.","DOI":"10.1111\/mice.12269"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6952","DOI":"10.3390\/s8116952","article-title":"A Novel Vehicle Classification Using Embedded Strain Gauge Sensors","volume":"8","author":"Wenbin","year":"2008","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Birgin, H., Laflamme, S., D\u2019Alessandro, A., Garc\u00eda-Mac\u00edas, E., and Ubertini, F. (2020). A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials. Sensors, 20.","DOI":"10.3390\/s20030659"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gautam, A., Singh, R.R., Kumar, A., and Thangaraj, J. (2018, January 9\u201311). FBG based sensing architecture for traffic surveillance in railways. Proceedings of the 2018 3rd International Conference on Microwave and Photonics (ICMAP), Dhanbad, India.","DOI":"10.1109\/ICMAP.2018.8354567"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Caprez, M., Doupal, E., Jacob, B., O\u2019Connor, A., and OBrien, E. (2000). Test of WIM sensors and systems on an urban road. Int. J. Heavy Veh. Syst., 7.","DOI":"10.1504\/IJHVS.2000.005003"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Th\u00f6ns, S., Limongelli, M.P., Ivankovic, A.M., Val, D., Chryssanthopoulos, M., Lombaert, G., D\u00f6hler, M., Straub, D., Chatzi, E., and K\u00f6hler, J. (2017). Progress of the COST Action TU1402 on the Quantification of the Value of Structural Health Monitoring. Structural Health Monitoring 2017, DEStech Publications, Inc.","DOI":"10.12783\/shm2017\/14002"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Matos, J., Casas, J., Strauss, A., and Fernandes, S. (2016). COST ACTION TU1406: Quality Specifications for Roadway Bridges, Standardization at a European level (BridgeSpec)\u2014Performance indicators. Performance-Based Approaches for Concrete Structures\u201414th fib Symposium Proceedings, fib.","DOI":"10.1201\/9781315207681-141"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"976","DOI":"10.2749\/222137816819259464","article-title":"Quality Specifications for Highway Bridges: Standardization and Homogenization at the European Level (COST TU-1406)","volume":"106","author":"Casas","year":"2016","journal-title":"Iabse Symp. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Haus, J. (2010). Optical Sensors: Basics and Applications, Wiley-VCH.","DOI":"10.1002\/9783527629435"},{"key":"ref_18","unstructured":"Yin, S., Ruffin, P.B., and Yu, F.T.S. (2008). Fiber Optic Sensors, CRC Press. [2nd ed.]. Number 132 in Optical Science and Engineering."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Venghaus, H. (2006). Wavelength Filters in Fibre Optics, Springer.","DOI":"10.1007\/3-540-31770-8"},{"key":"ref_20","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst., 25."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Phung, V.H., and Rhee, E.J. (2019). A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets. Appl. Sci., 9.","DOI":"10.3390\/app9214500"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kamencay, P., Benco, M., Mizdos, T., and Radil, R. (2017). A New Method for Face Recognition Using Convolutional Neural Network. Adv. Electr. Electron. Eng., 15.","DOI":"10.15598\/aeee.v15i4.2389"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., Cao, L., and Zhang, L. (2017). Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sens., 9.","DOI":"10.3390\/rs9080848"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Samir, S., Emary, E., El-Sayed, K., and Onsi, H. (2020). Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries. Information, 11.","DOI":"10.3390\/info11050275"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., Hua, X., and Zeng, X. (2020). Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network. Entropy, 22.","DOI":"10.3390\/e22050585"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, J.Y., Lee, H.E., Choi, Y.H., Lee, S.J., and Jeon, J.S. (2019). CNN-based diagnosis models for canine ulcerative keratitis. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-50437-0"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1109\/TVT.2019.2961625","article-title":"Real-Time Single-Stage Vehicle Detector Optimized by Multi-Stage Image-Based Online Hard Example Mining","volume":"69","author":"Lin","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3558","DOI":"10.1109\/TCSVT.2019.2906195","article-title":"Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveillance Videos","volume":"29","author":"Liu","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Frniak, M., Kamencay, P., Markovic, M., Dubovan, J., and Dado, M. (2020). Comparison of Vehicle Categorisation by Convolutional Neural Networks Using MATLAB, IEEE. ELEKTRO 2020 PROC.","DOI":"10.1109\/ELEKTRO49696.2020.9130238"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:58:47Z","timestamp":1760176727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,10]]},"references-count":30,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164472"],"URL":"https:\/\/doi.org\/10.3390\/s20164472","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,10]]}}}