An Analysis of the Implementation of Edge Detection Operators in FPGA
Resumo
Computer vision systems have several stages, and one of the operators used in these systems is the edge detection filter. High-performance computing is required in many applications and stages of computer vision systems, and many designs use FPGA technology to improve performance and decrease power consumption. In this context, this work presents an analysis of five edge detection filters synthesized to FPGA, including Laplacian, Roberts, Prewitt, Sobel, and Canny. In the experiments, we compared the hardware implementations with software versions to identify the impact of fixed-point representation on the quality of the output images. We have also assessed metrics regarding performance, silicon costs, and energy consumption. The results obtained show that the Laplacian filter has the lowest costs, while the Canny operator provides the best output image at the price of much higher silicon costs and energy consumption.
Palavras-chave:
Image Processing, Computer Vision, Edge Detection, Hardware Accelerator, FPGA
Referências
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M. Yih, J. M. Ota, J. D. Owens, and P. Muyan-Ozc¸elik, “FPGA versus¨ GPU for speed-limit-sign recognition,” in 2018 21st Int. Conf. on Intelligent Transportation Systems (ITSC). IEEE, 2018, pp. 843–850.
P. Ganesan and G. Sajiv, “A comprehensive study of edge detection for image processing applications,” in 2017 Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–6.
M. Mittal, A. Verma, I. Kaur, B. Kaur, M. Sharma, L. M. Goyal, S. Roy, and T. Kim, “An efficient edge detection approach to provide better edge connectivity for image analysis,” IEEE Access, vol. 7, pp. 33240–33255, 2019.
G. B. Reddy and K. Anusudha, “Implementation of image edge detection on FPGA using XSG,” in 2016 Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT), Mar. 2016, pp. 1–5.
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,” in 2015 Int. Conf. on Technologies for Sustainable Development (ICTSD), Feb. 2015, pp. 1–4.
J. Lee, H. Tang, and J. Park, “Energy efficient Canny edge detector for advanced mobile vision applications,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 4, pp. 1037–1046, Apr. 2018.
P. Selvakumar and S. Hariganesh, “The performance analysis of edge detection algorithms for image processing,” in 2016 Int. Conf. on Computing Technologies and Intelligent Data Engineering (ICCTIDE), Jan. 2016, pp. 1–5.
M. Qasaimeh, K. Denolf, J. Lo, K. A. Vissers, J. Zambreno, and P. H. Jones, “Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels,” in 2019 Int. Conf. on Embedded Software and Systems (ICESS), 2019, pp. 1–8.
C. Schlaak, M. Fakih, and R. Stemmer, “Power and execution time measurement methodology for SDF applications on FPGA-based MPSoCs,” in 2019 Int. Wksp. on High Performance Energy Efficient Embedded Systems (HIP3ES), 2017, pp. 1–7.
O. R. Vincent, O. Folorunso et al., “A descriptive algorithm for Sobel image edge detection,” in Proc. of Information Science & IT Education Conference (InSITE), vol. 40. Information Science Institute California, 2009, pp. 97–107.
S. S. Al-Amri, N. Kalyankar, and S. Khamitkar, “Image segmentation by using edge detection,” Int. J. on Computer Science and Engineering, vol. 2, no. 3, pp. 804–807, 2010.
L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recognition, vol. 34, no. 3, pp. 721–725, 2001.
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898–916, May 2011. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2010.161
J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, “Bi-directional cascade network for perceptual edge detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
A. Akbarinia, C. A. Parraga et al., “Biologically-inspired edge detection through surround modulation,” in Proceedings of the British Machine Vision Conference, 2016, pp. 1–13.
S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015.
P. Dollar and C. L. Zitnick, “Structured forests for fast edge detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2013.
X. Hu, Y. Liu, K. Wang, and B. Ren, “Learning hybrid convolutional features for edge detection,” Neurocomputing, vol. 313, pp. 377 – 385, 2018.
S. Asaly, B. Ben-Moshe, and N. Shvalb, “Accurate 3D mapping algorithm for flexible antennas,” Int. J. of Antennas and Propagation, vol. 2018, 2018.
C. Ttofis, C. Kyrkou, and T. Theocharides, “A low-cost real-time embedded stereo vision system for accurate disparity estimation based on guided image filtering,” IEEE Trans. on Computers, vol. 65, no. 9, pp. 2678–2693, Sep. 2016.
M. Yih, J. M. Ota, J. D. Owens, and P. Muyan-Ozc¸elik, “FPGA versus¨ GPU for speed-limit-sign recognition,” in 2018 21st Int. Conf. on Intelligent Transportation Systems (ITSC). IEEE, 2018, pp. 843–850.
P. Ganesan and G. Sajiv, “A comprehensive study of edge detection for image processing applications,” in 2017 Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–6.
M. Mittal, A. Verma, I. Kaur, B. Kaur, M. Sharma, L. M. Goyal, S. Roy, and T. Kim, “An efficient edge detection approach to provide better edge connectivity for image analysis,” IEEE Access, vol. 7, pp. 33240–33255, 2019.
G. B. Reddy and K. Anusudha, “Implementation of image edge detection on FPGA using XSG,” in 2016 Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT), Mar. 2016, pp. 1–5.
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,” in 2015 Int. Conf. on Technologies for Sustainable Development (ICTSD), Feb. 2015, pp. 1–4.
J. Lee, H. Tang, and J. Park, “Energy efficient Canny edge detector for advanced mobile vision applications,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 4, pp. 1037–1046, Apr. 2018.
P. Selvakumar and S. Hariganesh, “The performance analysis of edge detection algorithms for image processing,” in 2016 Int. Conf. on Computing Technologies and Intelligent Data Engineering (ICCTIDE), Jan. 2016, pp. 1–5.
M. Qasaimeh, K. Denolf, J. Lo, K. A. Vissers, J. Zambreno, and P. H. Jones, “Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels,” in 2019 Int. Conf. on Embedded Software and Systems (ICESS), 2019, pp. 1–8.
C. Schlaak, M. Fakih, and R. Stemmer, “Power and execution time measurement methodology for SDF applications on FPGA-based MPSoCs,” in 2019 Int. Wksp. on High Performance Energy Efficient Embedded Systems (HIP3ES), 2017, pp. 1–7.
O. R. Vincent, O. Folorunso et al., “A descriptive algorithm for Sobel image edge detection,” in Proc. of Information Science & IT Education Conference (InSITE), vol. 40. Information Science Institute California, 2009, pp. 97–107.
S. S. Al-Amri, N. Kalyankar, and S. Khamitkar, “Image segmentation by using edge detection,” Int. J. on Computer Science and Engineering, vol. 2, no. 3, pp. 804–807, 2010.
L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recognition, vol. 34, no. 3, pp. 721–725, 2001.
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898–916, May 2011. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2010.161
J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, “Bi-directional cascade network for perceptual edge detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
A. Akbarinia, C. A. Parraga et al., “Biologically-inspired edge detection through surround modulation,” in Proceedings of the British Machine Vision Conference, 2016, pp. 1–13.
S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015.
P. Dollar and C. L. Zitnick, “Structured forests for fast edge detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2013.
X. Hu, Y. Liu, K. Wang, and B. Ren, “Learning hybrid convolutional features for edge detection,” Neurocomputing, vol. 313, pp. 377 – 385, 2018.
Publicado
23/11/2020
Como Citar
SANTOS, Douglas; ZOLETT, Daniel; BELLI, Mateus; VIEL, Felipe; ZEFERINO, Cesar.
An Analysis of the Implementation of Edge Detection Operators in FPGA. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 10. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 163-167.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2020.13107.
