Image and Video Processing Applications Using Xilinx System Generator

Yaman, Sertaç; Yildrim, Merve; Karmshoglu, Bircan; Erol, Yavuz; Kürüm, Hasan

doi:10.1109/isdfs.2019.8757540

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…As for pixels with magnitudes between T1 and T2, they are referred to as a part of edge only when they have direct connections with a magtitude-above-T2 pixel [13]. The Canny edge detection provides better edge detection, better localization and direct response [14][15]. For Canny operators in this work, T1 was set to 110 and T2 was set to 220 for all experiments.…”

Section: Edge Detection Operatormentioning

confidence: 99%

2D image edge detection enhancement using convolutional neural network

Lin

2023

ACE

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Traditional edge detection operators are usually applied on edge detection in 2D image processing. However, the edge detection system equipped with simple operators has many disadvantages, such as high sensitivity to noise and neglect of significant edge details. This work proposed a method to enhance edge detection with convolutional neural net-work. To overcome the shortcomings of the system using simple edge detection operators in 2D image processing, an edge detection system using convolutional neural network was developed with Python language. In the convolutional neural network, two convolutional layers were designed to extract 2D image features that were relative to edge information. Then a normalization layer was applied to normalize the convoluted output. After that, pre-processing was utilized to denoise and smooth the image input. The final step was edge detection using traditional operators. Experiments were also implemented to verify the improvement of the plugin of the three-layer convolutional neural network in the de-signed edge detection system. Relative frequencies were utilized to quantify the edge de-tection performance. Results showed that the involvement of convolutional neural net-work could strengthen edge detection operators performance obviously in computer vi-sion.

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Section: Edge Detection Operatormentioning

confidence: 99%

2D image edge detection enhancement using convolutional neural network

Lin

2023

ACE

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“…The Canny edge detection provides better edge detection, better localization and direct response [11]. The Canny edge detection provides better edge detection, better localization and direct response [7]. In this paper, canny algorithm is selected for vehicle edge detection.…”

Section: Gy Gx mentioning

confidence: 99%

“…It is observed that the system could not classify vehicles. In [7], the work is proposed on image and video processing applications using Xilinx System Generator. The proposed design is only for vehicle detection with less accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Baloch¹,

Memon²,

Memon³

et al. 2021

IJEM

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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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