FPGA implementation of sobel edge detection algorithm
DOI:
https://doi.org/10.4108/eetiot.5148Keywords:
Sobel edge detection algorithm, Matlab, FPGAAbstract
Sobel Edge detection algorithm is used to extract the edges (region of maximum variation) from an image. It is based on the concept that the edges of an image contains maximum information whose computation depends on multipliers and square root. As multipliers consume more logic, a modified sobel edge detection algorithm which does not employ multipliers and square root function is proposed. A mathematical model of the proposed sobel edge algorithm was first developed and MATLAB was used to verify the model. On comparing with the original model, the proposed model has a SSIM of 96.43%. To analyse the hardware complexity, Verilog model of the modified sobel edge detection algorithm was developed using Quartus II. The chosen evaluation board is Cylone III FPGA EP3C120F780. The performance metrics such has Logic Elements utilization, Power dissipation and Maximum Operating Frequency were obtained. Open-Source toolchain (Yosys, OpenVPR, and Google Skywater 130nm PDK) was used to obtain the RTL Netlist and Synthesis reports. Verilog Modules for the Camera (CMOS OV7670) interface and FIFO Buffer were synthesized. The modified algorithm was integrated with them. An HSMC (HSMB) breakout board was connected to the FPGA Development board to increase the number of I/O ports. Thus in real time, the proposed modified Sobel Edge detection system can be used as a pre-processor to reduce the amount of computations and power consumption.
Downloads
References
[1] B. Saha Tchinda, D. Tchiotsop, M. Noubom, V. Louis-Dorr, and D. Wolf, “Retinal blood vessels segmentation using classical edge detection filters and the neural network,” Informatics in Medicine Unlocked, 23, 2021, p.100521, doi: 10.1016/j.imu.2021.100521.
[2] Y. H. Kwon and J. W. Jeon, "Comparison of FPGA Implemented Sobel Edge Detector and Canny Edge Detector", In Proceedings of the IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), 2020, pp. 1-2, doi: 10.1109/ICCE-Asia49877.2020.9277425.
[3] A. Abbasi, M. Abbasi, A proposed FPGA based architecture for sobel edge detection operator, J. Act. Passive Electron. Devices, 2, 2007.
[4] I. Yasri, N. H. Hamid, and V. V. Yap, “Performance analysis of FPGA based Sobel edge detection operator,” 2008 International Conference on Electronic Design, Dec. 2008, doi: 10.1109/ICED.2008.4786751.
[5] S. Halder, D. Bhattacharjee, M. Nasipuri, D.K. Basu, A Fast FPGA Based Architecture for Sobel Edge Detection, Springer, 2012.
[6] J. Monson, M. Wirthlin, B. L. Hutchings, “Optimization techniques for a high level synthesis implementation of the Sobel filter”, In Proceedings of the International Conference on Reconfigurable Computing and FPGAs (ReConFig), 2013, pp. 1-6. https://doi.org/10.1109/ReConFig.2013.6732315.
[7] G. Chaple, R.D. Daruwala, “Design of Sobel operator based image edge detection algorithm on FPGA”, In Proceedings of the International Conference on Communication and Signal Processing, 2014, pp. 788-792. https://doi.org/10.1109/ICCSP.2014.6949951
[8] M. Chouchene, F. E. Sayadi, Y. Said, M. Atri, and R. Tourki, “Efficient implementation of Sobel edge detection algorithm on CPU, GPU and FPGA”, International Journal of Advanced Media and Communication, 5,(2/3), 2014, p.105.
[9] M. Amiri, F. M. Siddiqui, C. Kelly, “FPGA-Based Soft-Core Processors for Image Processing Applications” J Sign Process Syst, 87, 2017, pp. 139–156. https://doi.org/10.1007/s11265-016-1185-7
[10] K. Zhang, Y. Zhang, P. Wang, Y. Tian, and J. Yang, “An improved sobel edge algorithm and FPGA implementation,” Procedia Computer Science, 131, 2018, pp. 243–248, doi: 10.1016/j.procs.2018.04.209.
[11] N. Nausheen, A. Seal, P. Khanna, S. Halder, “A FPGA based implementation of Sobel edge detection,” Science Direct, Microprocessors and Microsystems, 56, 2018, pp. 84-91, https://doi.org/10.1016/j.micpro.2017.10.011.
[12] Z. Xiangxi, Z. Yonghui, Z. Shuaiyan, Z. Jian, “FPGA implementation of edge detection for Sobel operator in eight directions”, In Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems, Chengdu. 2018, pp. 520-523.
[13] K. Hill, S. Craciun, A. George, H. Lam, “Comparative analysis of OpenCL vs. HDL with image-processing kernels on stratix-v FPGA”, In Proceedings of the IEEE 26th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2015, https://doi.org/10.1109/asap.2015.7245733.
[14] H. Waidyasooriya, M. Hariyama, and K. Uchiyama, “Design of FPGA-based computing systems with OpenCL”, Springer International Publishing, 2018, https://doi.org/10.1007/978-3-319-68161-0
[15] D. R. Menaka, D. R. Janarthanan, and D. K. Deeba, “FPGA implementation of low power and high speed image edge detection algorithm,” Microprocessors and Microsystems, 75, 2020, p. 103053, doi: 10.1016/j.micpro.2020.103053.
[16] G. K. Ijemaru et al., “Image processing system using matlab-based analytics,” Bulletin of Electrical Engineering and Informatics, 10, (5), 2021, pp. 2566–2577, doi: 10.11591/eei.v10i5.3160.
[17] M. A. Dávila-Guzmán, R. G. Tejero, M. Villarroya-Gaudó, D. S. Gracia, L. Kalms, and D. Göhringer, “A Cross-platform openVX library for FPGA accelerators,” In Proceedings of the 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021, 2021, pp. 75–83, doi: 10.1109/PDP52278.2021.00020.
[18] Ahmed Khazal Younis, Basma MohammedKamal Younis, Mohammed Sabah Jarjees, “Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array”, Indonesian Journal of Electrical Engineering and Computer Science, 26, (1), 2022, pp. 86~95 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v26.i1.pp86-95.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
