• Ajay Kumar

Due to the heavy use of vehicles in transport, classification of vehicles is critical. The speed and tires of the vehicles can then be estimated at different times when the vehicles arrives at the sensor location and the speed is monitored by changes the rest of the travel sensor. The vehicle classification system in this article uses machine learning vector (SVM) algorithms to categorize vehicles into class from small vehicles to compact trucks. Traffic analysis consequences demonstrate that development systems can better evaluate vehicles classification. The vehicles are classified depending on the number of axes and the space (wheels) between them. The accuracy of the vehicles classification depends on the accuracy of the speed and steering wheel. In this study, a three-nine-gauge glass plate with Bragg grating sensor (3D GFRP-FBG) was presented to evaluate the speed of the vehicles and wheels. The ability of sensors to estimate the speed of vehicles and tires in more sophisticated search engines..

SVM,FBG, Sensor, 3D GFRP,Fiber

"Fiber Bragg Grating Sensors based Vehicle Classification System Using SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.558-563, February-2020, Available :http://www.jetir.org/papers/JETIR2002289.pdf

"Fiber Bragg Grating Sensors based Vehicle Classification System Using SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp558-563, February-2020, Available at : http://www.jetir.org/papers/JETIR2002289.pdf