Hardware Synthesize and Performance Analysis of Intelligent Transportation Using Canny Edge Detection Algorithm

Full Text (PDF, 619KB), PP.22-32

Views: 0 Downloads: 0


Aisha Baloch 1,* Tayab D Memon 2,3 Farida Memon 2 Bharat Lal 2 Ved Viyas 1 Tony Jan 3

1. Institute of Information Communication and Technologies, Mehran University of Engineering and Technology, Jamshoro, Pakistan

2. Department of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.

3. School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, Australia

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2021.04.03

Received: 10 Apr. 2021 / Revised: 20 May 2021 / Accepted: 16 Jun. 2021 / Published: 8 Aug. 2021

Index Terms

Intelligent Transportation, vehicle detection and classification, Xilinx system generator, Zedboard FPGA board, Xilinx Platform, DVI connector.


The World is moving toward Smart traffic management and monitoring technologies. Vehicle detection and classification are the two important features of intelligent transportation system. Several algorithms for detection of vehicles such as Sobel, Prewitt, and Robert etc. but due to their less accuracy and sensitivity to noise they could not detect vehicles clearly. In this paper, a simple and rapid prototyping approach for vehicle detection and classification using MATLAB Xilinx system generator and Zedboard is presented. The Simulink model of vehicle detection and classification is designed using a complex canny edge detection algorithm for vehicle detection. The canny edge detection algorithm offers 91% accuracy as compared to its counterpart Sobel and Perwitt algorithms that offer 79.4% and 76.1% accuracy. The feature vector approach is used for vehicle classification. The proposed model is simulated and validated in MATLAB. The Canny edge detection and feature vector algorithms for vehicle detection and classification are synthesized through the Xilinx system generator in Zedboard. The proposed design is validated with the existing works. The implementation results reveal that the proposed system for vehicle detection and classification takes only 8 ns of execution time with a 128MHz clock, which is the lowest and optimum calculation period for the smart city.

Cite This Paper

Aisha Baloch, Tayab D Memon, Farida Memon, Bharat Lal, Ved Viyas, Tony Jan, " Hardware Synthesize and Performance Analysis of Intelligent Transportation Using Canny Edge Detection Algorithm", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.4, pp. 22-32, 2021. DOI: 10.5815/ijem.2021.04.03


[1]S. Kul, S. Eken, and A. Sayar, “A concise review on vehicle detection and classification,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-January, no. January 2019, pp. 1–4, 2018, doi: 10.1109/ICEngTechnol.2017.8308199.

[2]M. P. H. Pawar and P. R. P. Patil, “FPGA Implementation of Canny Edge Detection Algorithm,” Int. J. Eng. Comput. Sci., vol. 3, no. 10, pp. 8704–8709, 2014.

[3]G. N. Chaple, R. D. Daruwala, and M. S. Gofane, “Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA,” Proc. - Int. Conf. Technol. Sustain. Dev. ICTSD 2015, no. 1, pp. 31–34, 2015, doi: 10.1109/ICTSD.2015.7095920.

[4]S. M. Alex Raj and M. H. Supriya, “Hardware Co-simulation of underwater moving object detection using Xilinx system generator,” Int. J. Ocean. Oceanogr., vol. 10, no. 1, pp. 73–80, 2016.

[5]S. G. Kavitkar, “FPGA based Image Feature Extraction Using Xilinx System Generator,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 3, pp. 3743–3747, 2014.

[6]T. Kryjak, M. Komorkiewicz, and M. Gorgon, “Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC,” J. Real-Time Image Process., vol. 15, no. 1, pp. 123–159, 2018, doi: 10.1007/s11554-016-0588-9.

[7]S. Yaman, M. Yildirim, B. Kamişlioǧlu, Y. Erol, and H. Kürüm, “Image and video processing applications using Xilinx system generator,” 7th Int. Symp. Digit. Forensics Secur. ISDFS 2019, 2019, doi: 10.1109/ISDFS.2019.8757540.

[8]G. Baraskar and P. Thakre, “Evaluation of Canny and Sobel Edge Detection Technique using Xilinx System Generator,” vol. 2, no. 2, pp. 53–56, 2017, [Online]. Available: http://ijsrst.com/paper/872.pdf.

[9]A. Arinaldi, J. A. Pradana, and A. A. Gurusinga, “Detection and classification of vehicles for traffic video analytics,” Procedia Comput. Sci., vol. 144, pp. 259–268, 2018, doi: 10.1016/j.procs.2018.10.527.

[10]V. Keerthi Kiran, P. Parida, and S. Dash, Vehicle detection and classification: A review, vol. 1180 AISC, no. January. Springer International Publishing, 2021.

[11]L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” Proc. - 2015 4th Int. Conf. Adv. Inf. Technol. Sens. Appl. AITS 2015, pp. 28–31, 2016, doi: 10.1109/AITS.2015.14.

[12]P. P. V. Keerthi Kiran, “Vehicle Detection and Classification: A Review’’.”

[13]S. Thepade, R. Das, and S. Ghosh, “A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification,” J. Eng. (United Kingdom), vol. 2014, 2014, doi: 10.1155/2014/439218.

[14]G. Baraskar and P. Thakre, “Evaluation of Canny and Sobel Edge Detection Technique using Xilinx System Generator,” vol. 2, no. 2, pp. 53–56, 2017, [Online]. Available: http://ijsrst.com/paper/872.pdf.

[15]K. Kumar, R. K. Mishra, and D. Nandan, “Efficient Hardware of RGB to Gray Conversion Realized on FPGA and ASIC,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 2008–2015, 2020, doi: 10.1016/j.procs.2020.04.215.

[16]A. R. Bhagat, S. R. Dixit, M. T. Student, T. Engineering, and T. Engineering, “VHDL based Sobel Edge Detection,” vol. 3, no. 1, pp. 1217–1223, 2015.

[17]Q. Xu, S. Varadarajan, C. Chakrabarti, and L. J. Karam, “A distributed canny edge detector: Algorithm and FPGA implementation,” IEEE Trans. Image Process., vol. 23, no. 7, pp. 2944–2960, 2014, doi: 10.1109/TIP.2014.2311656.

[18]K. Bala Krishnan, S. Prakash Ranga, and N. Guptha, “A Survey on Different Edge Detection Techniques for Image Segmentation,” Indian J. Sci. Technol., vol. 10, no. 4, 2017, doi: 10.17485/ijst/2017/v10i4/108963.

[19]R. Mehra and R. Verma, “Area Efficient FPGA Implementation of Sobel Edge Detector for Image Processing Applications,” Int. J. Comput. Appl., vol. 56, no. 16, pp. 7–11, 2012, doi: 10.5120/8973-3086.

[20]S. Parveen and S. Lasrado, “Computation of traffic density in video streams using XSG blockset,” RTEICT 2017 - 2nd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. Proc., vol. 2018-January, pp. 1248–1252, 2017, doi: 10.1109/RTEICT.2017.8256798.

[21]H. M. Abdelgawad, M. Safar, and A. M. Wahba, “High level synthesis of canny edge detection algorithm on Zynq platform,” Int. J. Comput. Electr. Autom. Control Inf. Eng., vol. 9, no. 1, pp. 148–152, 2015, [Online]. Available: http://www.waset.org/publications/10000239.

[22]S. Vijayarani, M. M. Vinupriya, and A. Professor, “Performance Analysis of Canny and Sobel Edge Detection Algorithms in Image Mining,” Int. J. Innov. Res. Comput. Commun. Eng. (An ISO Certif. Organ., vol. 3297, no. 8, pp. 1760–1767, 2007, [Online]. Available: www.ijircce.com.

[23]S. Karanwal, “Implementation of Edge Detection at Multiple Scales,” Int. J. Eng. Manuf., vol. 11, no. 1, pp. 1–10, 2021, doi: 10.5815/ijem.2021.01.01.

[24]R. R, N. Saklani, and V. Verma, “A Review on Edge detection Technique ‘Canny Edge Detection,’” Int. J. Comput. Appl., vol. 178, no. 10, pp. 28–30, 2019, doi: 10.5120/ijca2019918828.

[25]R. Javadzadeh, E. Banihashemi, and J. Hamidzadeh, “Fast Vehicle Detection and Counting Using Background Subtraction Technique and Prewitt Edge Detection,” Int. J. Comput. Sci. Telecommun. J. Homepage www.ijcst.org, vol. 6, no. 10, pp. 8–12, 2015, [Online]. Available: http://www.ijcst.org/Volume6/Issue10/p2_6_10.pdf.

[26]D. Kumari and K. Kaur, “A Survey on Stereo Matching Techniques for 3D Vision in Image Processing,” Int. J. Eng. Manuf., vol. 6, no. 4, pp. 40–49, 2016, doi: 10.5815/ijem.2016.04.05.

[27]N. Kumala Dewi, “Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept,” Int. J. Eng. Manuf., vol. 11, no. 1, pp. 29–36, 2021, doi: 10.5815/ijem.2021.01.04.