Design of Matlab/Simulink-based Edge Detection operators and hardware implementation on ZYNQ FPGA

Edge detection is an algorithm that uses specialized kernels to capture boundary features of the image in the domains of vision research. Edge-detection algorithms can be accomplished to a large extent at the software extraction level, yet implementing it via FPGA can be significant for local edge recognition in images. Edge detection is an interaction to recognize boundaries from digital images by distinguishing brightness disparities. The paper presents diverse edge-detection techniques. This strategy incorporates morphological local edge detection and contrast enhancement. The paper presents a SIMULINK-based edge-detection model for operators including Prewitt, Sobel, and Robert and their corresponding simulation results. The boundaries of the region of interest are extracted as features from chest radiographs. The assessment of processed standards is significant in imaging applications. Image standard assessment is allied to similarity assessment where the standard is assessed on the distinction between a primary and a processed image. The edge detection models are implemented on EDGE ZYNQ SoC FPGA Development Board. Using an IP core built on Verilog, the input images are read from storage and the edge-detected images are written to storage. All tasks have been effectively accomplished via Xilinx Verilog code that is compiled using the Vivado and SDK programming tools. An EDGE ZYNQ SoC FPGA Development Board is then used to execute the algorithm, and computational results are derived. The findings of the Simulink implementation are used to substantiate the FPGA simulation results. According to the findings, the edge-detection algorithm is designed and implemented successfully on the EDGE ZYNQ SoC FPGA Development Board.