Document Type : Research Note


Faculty of Electrical Engineering, Islamic Azad University Majlisi Branch, Isfahan, Iran.


Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, smuggling detection, and so on. In this paper, various techniques such as data augmentation and space filtering have been used to improve and enhance the data. Then, a developed algorithm that integrates VGG neural network and YOLO algorithm has been used to detect and identify vehicles, Then the implementation on the Raspberry hardware board and practically through a scenario is mentioned. Real including image data sets are analyzed. The results show the good performance of the implemented algorithm in terms of detection performance (98%), processing speed, and environmental conditions, which indicates its capability in practical applications with low cost.


[1] B. Stewart, I. Reading, M. Thomson, T. Binnie, K. Dickinson, and C. Wan, "Adaptive lane finding in road traffic image analysis," in Seventh International Conference on Road Traffic Monitoring and Control, 1994., 1994, pp. 133-136: IET.
[2] Enkelmann, W. (1991). "Obstacle detection by evaluation of optical flow fields from image sequences." Image and Vision Computing,1991, 9(3), 160-168.
[3] Won Y, Nam J, Lee BH. "Image pattern recognition in natural environment using morphological feature extraction." InJoint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) 2000 Aug 30 (pp. 806-815). Springer, Berlin, Heidelberg.
[4] Li J, Liu Y, Tageldin A, Zaki MH, Mori G, Sayed T. "Automated region-based vehicle conflict detection using computer vision techniques." Transportation Research Record. 2015 Jan;2528(1):49-59.
[5] Kim JB, Park HS, Park MH, Kim HJ. "A real-time region-based motion segmentation using adaptive thresholding and K-means clustering." InAustralian joint conference on artificial intelligence 2001 Dec 10 (pp. 213-224). Springer, Berlin, Heidelberg.
[6] Hadi RA, Sulong G, George LE. "Vehicle detection and tracking techniques:" a concise review. arXiv preprint arXiv:1410.5894. 2014 Oct 22.
[7] Oliveira M, Santos V. "Automatic detection of cars in real roads using haar-like features." Department of Mechanical Engineering, University of Aveiro. 2008 Jul;3810.
[8] Dubuisson MP, Jain AK. "Contour extraction of moving objects in complex outdoor scenes." International Journal of Computer Vision. 1995 Jan;14(1):83-105.
[9] Hicham B, Ahmed A, Mohammed M. "Vehicle type classification using convolutional neural network." In2018 IEEE 5th International Congress on Information Science and Technology (CiSt) 2018 Oct 21 (pp. 313-316). IEEE.
[10] Nasehi M, Ashourian M, Moalem P. "An Overview of the Type of Vehicle Detection Techniques." Majlesi Journal of Telecommunication Devices. 2020 Sep 1;9(3).
[11] Zhigang Z, Huan L, Pengcheng D, Guangbing Z, Nan W, Wei-Kun Z. "Vehicle target detection based on R-FCN." In2018 Chinese Control And Decision Conference (CCDC) 2018 Jun 9 (pp. 5739-5743). IEEE.
[12] Li Y, Song B, Kang X, Du X, Guizani M. "Vehicle-type detection based on compressed sensing and deep learning in vehicular networks." Sensors. 2018 Dec 19;18(12):4500.
[13] Wang X, Zhang W, Wu X, Xiao L, Qian Y, Fang Z. "Real-time vehicle type classification with deep convolutional neural networks." Journal of Real-Time Image Processing. 2019 Feb;16(1):5-14.
[14] Sheng M, Liu C, Zhang Q, Lou L, Zheng Y. "Vehicle detection and classification using convolutional neural networks." In2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) 2018 May 25 (pp. 581-587). IEEE.
[15] Vedaldi A, Zisserman A. "Vgg convolutional neural networks practical." Department of Engineering Science, University of Oxford. 2016 Oct;66.
[16] Huang R, Pedoeem J, Chen C. "YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers." In2018 IEEE International Conference on Big Data (Big Data) 2018 Dec 10 (pp. 2503-2510). IEEE.
[17] Sharifi-Tehrani O. "Hardware Design of Image Channel Denoiser for FPGA Embedded Systems." Przegląd Elektrotechniczny. 2012 Jan 1;88(3b):165-7.
[18] Sharifi-Tehrani O. "Novel hardware-efficient design of LMS-based adaptive FIR filter utilizing Finite State Machine and Block-RAM." Przeglad Elektrotechniczny. 2011 Jul;87(7):240-4.
[19] Tang T, Zhou S, Deng Z, Zou H, Lei L. "Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining." Sensors. 2017 Feb 10;17(2):336.