Saeid Alizadeh - Department of Electrical Engineering, Montazeri Technical University, Mashhad, Iran
Iman Bagheri - Department of Biomedical Engineering, International University of Imam Reza (AS), Mashhad, Iran
Mohammed Bushara Jarma - Department of Mechanical Engineering, University of Khartoum, Khartoum, Sudan
Masoumeh Vahedi - Department of the Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Elyas Irankhah - Department of Biomedical Engineering, International University of Imam Reza (AS), Mashhad, Iran

The increase in road accidents and definitely regarding human deaths have led to the implementation of high-quality intelligent traffic transportation systems. In this research, using the vehicle detection system including license plate detection, a novel method for reducing accidents related to lane weaving happening in both highways and rural areas has been presented. In this method, the vehicle s detection system records 45 frames within a time of 1.5 seconds, and after passing the frames through several filters, the targeted vehicle is identified using the Convolutional Neural Network (CNN). The license plate of the aforesaid vehicle has also been identified using image enhancement, edge detection, and optical character recognition (OCR) techniques.

Vehicle detection - CNN- License plate detection - Lane Weaving- OCR