{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:22:05Z","timestamp":1770139325299,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research, Development, and Innovation Fund of Hungary","award":["142790"],"award-info":[{"award-number":["142790"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set.<\/jats:p>","DOI":"10.3390\/s22239299","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Real-Time Vehicle Classification System Using a Single Magnetometer"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4050-8231","authenticated-orcid":false,"given":"Peter","family":"Sarcevic","sequence":"first","affiliation":[{"name":"Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, Moszkvai Krt. 9, 6725 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Szilveszter","family":"Pletl","sequence":"additional","affiliation":[{"name":"Department of Technical Informatics, Faculty of Science and Informatics, University of Szeged, Arpad Ter 2, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9554-9586","authenticated-orcid":false,"given":"Akos","family":"Odry","sequence":"additional","affiliation":[{"name":"Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, Moszkvai Krt. 9, 6725 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","unstructured":"Cheung, S., and Varaiya, P. (2007). Traffic Surveillance by Wireless Sensor Networks: Final Report, University of California."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gheorghiu, R.A., Iordache, V., and Stan, V.A. (2021, January 29\u201330). Urban traffic detectors\u2014Comparison between inductive loop and magnetic sensors. Proceedings of the International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania.","DOI":"10.1109\/ECAI52376.2021.9515014"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"473","DOI":"10.2478\/mms-2014-0040","article-title":"A highly selective vehicle classification utilizing dual-loop inductive detector","volume":"21","author":"Gajda","year":"2014","journal-title":"Metrol. Meas. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1109\/TVT.2010.2049756","article-title":"Vehicle-classification algorithm based on component analysis for single-loop inductive detector","volume":"59","author":"Meta","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"619","DOI":"10.2478\/mms-2014-0048","article-title":"Automatic vehicle classification in systems with single inductive loop detector","volume":"21","author":"Gajda","year":"2014","journal-title":"Metrol. Meas. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/TITS.2017.2741507","article-title":"Intelligent vehicle counting and classification sensor for real-time traffic surveillance","volume":"19","author":"Balid","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","first-page":"5875398","article-title":"Enhanced magnetic wireless sensor network algorithm for traffic flow monitoring in low-speed congested traffic","volume":"2020","author":"Fimbombaya","year":"2020","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24709","DOI":"10.1109\/JSEN.2021.3112161","article-title":"Magnetic sensor-based multi-vehicle data association","volume":"21","author":"Feng","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Valenti, G., Biral, F., and Fontanelli, D. (2021, January 2\u20133). Vehicle Localisation using asphalt embedded magnetometer sensors. Proceedings of the IEEE International Workshop on Metrology for Automotive (MetroAutomotive), Bologna, Italy.","DOI":"10.1109\/MetroAutomotive50197.2021.9502871"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.measurement.2010.09.044","article-title":"Vehicle detection and classification by measuring and processing magnetic signal","volume":"44","author":"Lan","year":"2011","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TITS.2014.2298199","article-title":"Classification of driving direction in traffic surveillance using magnetometers","volume":"15","author":"Hostettler","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TMAG.2012.2191299","article-title":"A practicable method for ferromagnetic object moving direction identification","volume":"48","author":"Zhu","year":"2012","journal-title":"IEEE Trans. Magn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13978","DOI":"10.3390\/s131013978","article-title":"Accelerometer-based event detector for low-power applications","volume":"13","author":"Smidla","year":"2013","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4917","DOI":"10.1109\/TVT.2014.2382644","article-title":"Vehicle tracking based on fusion of magnetometer and accelerometer sensor measurements with particle filtering","volume":"64","author":"Hostettler","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/TITS.2013.2273488","article-title":"A wireless accelerometer-based automatic vehicle classification prototype system","volume":"15","author":"Ma","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kleyko, D., Hostettler, R., Birk, W., and Osipov, E. (2015, January 15\u201318). Comparison of machine learning techniques for vehicle classification using road side sensors. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.100"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Miklusis, D., Markevicius, V., Navikas, D., Cepenas, M., Balamutas, J., Valinevicius, A., Zilys, M., Cuinas, I., Klimenta, D., and Andriukaitis, D. (2021). Research of distorted vehicle magnetic signatures recognitions, for length estimation in real traffic conditions. Sensors, 21.","DOI":"10.3390\/s21237872"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/JSEN.2014.2362122","article-title":"A parking occupancy detection algorithm based on AMR sensor","volume":"15","author":"Zhang","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"361242","DOI":"10.1155\/2015\/361242","article-title":"A vehicle parking detection method based on correlation of magnetic signals","volume":"2015","author":"Zhu","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/JSEN.2016.2523601","article-title":"A cross-correlation technique for vehicle detections in wireless magnetic sensor network","volume":"16","author":"Zhu","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TITS.2013.2273876","article-title":"Portable roadside sensors for vehicle counting, classification, and speed measurement","volume":"15","author":"Taghvaeeyan","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1109\/JSEN.2010.2103937","article-title":"Wireless magnetic sensor node for vehicle detection with optical wake-up","volume":"11","author":"Sifuentes","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Markevicius, V., Navikas, D., Zilys, M., Andriukaitis, D., Valinevicius, A., and Cepenas, M. (2016). Dynamic vehicle detection via the use of magnetic field sensors. Sensors, 16.","DOI":"10.3390\/s16010078"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Burresi, G., and Giorgi, R. (2015, January 14\u201318). A field experience for a vehicle recognition system using magnetic sensors. Proceedings of the Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro.","DOI":"10.1109\/MECO.2015.7181897"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s12046-017-0638-4","article-title":"Implementation of the vehicle recognition systems using wireless magnetic sensors","volume":"42","author":"Vancin","year":"2017","journal-title":"Sadhana"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/JSEN.2014.2359014","article-title":"Vehicle detection and classification for low-speed congested traffic with anisotropic magnetoresistive sensor","volume":"15","author":"Yang","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5247","DOI":"10.1109\/ACCESS.2018.2791446","article-title":"Improved robust vehicle detection and identification based on single magnetic sensor","volume":"6","author":"Dong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Markevicius, V., Navikas, D., Idzkowski, A., Andriukaitis, D., Valinevicius, A., and Zilys, M. (2018). Practical methods for vehicle speed estimation using a microprocessor-embedded system with AMR sensors. Sensors, 18.","DOI":"10.3390\/s18072225"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1109\/TITS.2017.2723908","article-title":"Roadside magnetic sensor system for vehicle detection in urban environments","volume":"19","author":"Wang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hodon, M., Karpis, O., Sevcik, P., and Kocianova, A. (2021). Which digital-output MEMS magnetometer meets the requirements of modern road traffic survey?. Sensors, 21.","DOI":"10.3390\/s21010266"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9510908","DOI":"10.1109\/TIM.2022.3198755","article-title":"Development of a MEMS-based IoV system for augmenting road traffic survey","volume":"71","author":"Spandonidis","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tafish, H., Balid, W., and Refai, H. (2016, January 5\u20139). Cost effective vehicle classification using a single wireless magnetometer. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus.","DOI":"10.1109\/IWCMC.2016.7577056"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tan, G., Ding, N., Shang, Y., and Lin, M. (2008, January 12\u201315). Vehicle classification algorithm based on binary proximity magnetic sensors and neural network. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), Beijing, China.","DOI":"10.1109\/ITSC.2008.4732522"},{"key":"ref_34","first-page":"274","article-title":"Wireless magnetic sensor network for road traffic monitoring and vehicle classification","volume":"17","author":"Velisavljevic","year":"2016","journal-title":"Transp. Telecommun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"16666","DOI":"10.1109\/JIOT.2021.3074907","article-title":"SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"9976","DOI":"10.1109\/JSEN.2019.2928828","article-title":"Vehicle classification based on feature selection with anisotropic magnetoresistive sensor","volume":"19","author":"Zhang","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52622","DOI":"10.1109\/ACCESS.2019.2908006","article-title":"Road vehicle detection and classification using magnetic field measurement","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"126814","DOI":"10.1109\/ACCESS.2020.3008483","article-title":"Vehicle classification and speed estimation based on a single magnetic sensor","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.measurement.2014.04.028","article-title":"Vehicle classification with single multi-functional magnetic sensor and optimal MNS-based CART","volume":"55","author":"Li","year":"2014","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TITS.2020.3024652","article-title":"MagMonitor: Vehicle speed estimation and vehicle classification through a magnetic sensor","volume":"23","author":"Feng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kaewkamnerd, S., Chinrungrueng, J., Pongthornseri, R., and Dumnin, S. (2010, January 20\u201323). Vehicle classification based on magnetic sensor signal. Proceedings of the International Conference on Information and Automation (ICIA), Harbin, China.","DOI":"10.1109\/ICINFA.2010.5512140"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1002\/atr.1299","article-title":"Improved waveform-feature-based vehicle classification using a single-point magnetic sensor","volume":"49","author":"He","year":"2015","journal-title":"J. Adv. Transp."},{"key":"ref_44","unstructured":"Sarcevic, P., and Pletl, S. (2014, January 9\u201313). Vehicle classification and false detection filtering using a single magnetic detector based intelligent sensor. Proceedings of the International Conference on Information Society and Technology (ICIST), Kopaonik, Serbia."},{"key":"ref_45","unstructured":"Sarcevic, P. (2019). New Methods in the Application of Inertial and Magnetic Sensors in Online Pattern Recognition Problems. [Ph.D. Thesis, University of Szeged]."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sarcevic, P., and Pletl, S. (2018, January 13\u201315). False detection filtering method for magnetic sensor-based vehicle detection systems. Proceedings of the IEEE International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia.","DOI":"10.1109\/SISY.2018.8524716"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12652-017-0606-1","article-title":"Online human movement classification using wrist-worn wireless sensors","volume":"10","author":"Sarcevic","year":"2019","journal-title":"J. Ambient Intell. Humaniz. 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