Abstract
Real-time image processing plays an important role in object detection in various fields, such as electric vehicle and security system. Image processing contains complex algorithms that require more processing time and consume lots of power for a CPU to solve. On the other hand, field programmable gate array (FPGA) having high computational power and capability of working with CPU solves the complex algorithm with more speed and less power consumption. Utilization of energy and resource is important parameter to make any system effective. In this paper, we develop an intellectual property (IP) for single-object detection using xfOpenCV library APIs. The detection contains libraries such as thresholding, Fast corner and Boundary Scan. The aim is to increase the ability of data processed by the system and save the power consumed by CPU by using FPGA. Such effective system can be used where power plays an important role and limited battery is available such as in electric vehicle. The targeted board is Zybo Zynq7000, which is more reliable and faster than CPU.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
M. Ning, A SoC-based acceleration method for UAV runway detection image pre-processing algorithm, in 25th International Conference on Automation and Computing (ICAC)(2019)
L. Vashist, M. Kumar, Design of Canny Edge Detection Hardware Accelerator Using xfOpenCV Library (Springer, Berlin, 2019)
A. Ben Amara, E. Pissaloux, M. Atri, Sobel edge detection system design and inte- gration on an FPGA based HD video streaming architecture, in 11th International Design and Test Symposium (IDT)(2016)
W.L. Wenchao Liu, H.C. He Chen, L.M. Long Ma, Moving object detection and tracking based on ZYNQ FPGA and ARM SOC, in IET International Radar Conference(2015)
Xilinx. Opencv Guide. Available: https://www.xilinx.com/support/documentation/sw_manuals/xilinx2017_1/ug1233-xilinx-opencv-user-guide.pdf
S. Chhabra, H. Jain, S. Saini, FPGA based hardware implementation of automatic vehicle license plate detection system, in International Conference on Advances in Computing, Communications and Informatics (ICACCI)(2016)
A. Cortes, I. Velez, A. Irizar, High level synthesis using vivado HLS for Zynq SoC: image processing case studies, in Conference on Design of Circuits and Integrated Systems (DCIS)(2016)
M. Kowalczyk, D. Przewlocka, T. Krvjak, Real-time implementation of contextual image processing operations for 4K video stream in Zynq UltraScale+ MPSo, in Conference on Design and Architectures for Signal and Image Processing (DASIP) (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saxena, A., Prasad, M.P.R., Sutar, P.S. (2021). Single-Object Detection Hardware Accelerator Using XfOpenCV Library. In: Vadhera, S., Umre, B.S., Kalam, A. (eds) Latest Trends in Renewable Energy Technologies. Lecture Notes in Electrical Engineering, vol 760. Springer, Singapore. https://doi.org/10.1007/978-981-16-1186-5_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-1186-5_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1185-8
Online ISBN: 978-981-16-1186-5
eBook Packages: EnergyEnergy (R0)