Visible to the public Implementation of a scalable real time canny edge detector on programmable SOC

TitleImplementation of a scalable real time canny edge detector on programmable SOC
Publication TypeConference Paper
Year of Publication2017
AuthorsMaheshwari, B. C., Burns, J., Blott, M., Gambardella, G.
Conference Name2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)
Date Publishednov
KeywordsAlgorithm design and analysis, Canny edge detection, Computer vision, Detectors, field programmable gate arrays, FPGA based acceleration, Hardware, Image edge detection, pubcrawl, PYNQ-Z1, Real-time Systems, Scalability, scalable, Scalable Security, security, Streaming media
Abstract

In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.

URLhttps://ieeexplore.ieee.org/document/8251977/
DOI10.1109/ICECTA.2017.8251977
Citation Keymaheshwari_implementation_2017