Farida Memon

Work place: Department of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.

E-mail: farida.memon@faculty.muet.edu.pk


Research Interests: Image Processing, Embedded System, Computational Learning Theory


Dr. Farida Memon is an Associate Professor in Department of Electronics Engineering Mehran University of Engineering & Technology Jamshoro, Sindh Pakistan, having more than 25 years of teaching & research experience. She holds a PhD in Electronic Engineering from Mehran University of Engineering & Technology (MUET) Jamshoro, Pakistan. She Received the B.E degree in Electronic Engineering and M.E degree in Telecommunication and Control 1993 and 2008 respectively. She has 33 National & International Journal and Conference publications. Her research interest lies in FPGAs, Image Processing, Machine Learning, Deep Learning, Embedded and Digital Systems Design.

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

By Aisha Baloch Tayab D Memon Farida Memon Bharat Lal Ved Viyas Tony Jan

DOI: https://doi.org/10.5815/ijem.2021.04.03, Pub. Date: 8 Aug. 2021

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.

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