计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 530-533.doi: 10.11896/j.issn.1002-137X.2017.11A.112

• 综合、交叉与应用 • 上一篇    下一篇

基于Zynq的图像角点及边缘检测系统的设计与实现

潘青松,张怡,杨宗明,秦剑秀   

  1. 西南交通大学 成都610031,西南交通大学 成都610031,西南交通大学 成都610031,西南交通大学 成都610031
  • 出版日期:2018-12-01 发布日期:2018-12-01

Design and Implementation of Image Corner and Edge Detection System Based on Zynq

PAN Qing-song, ZHANG Yi, YANG Zong-ming and QIN Jian-xiu   

  • Online:2018-12-01 Published:2018-12-01

摘要: 以Zynq芯片为基础,采用软硬件协同设计的方法设计并实现整个系统。Zynq芯片内部采用ARM+FPGA的异构架构,既具备ARM处理器的灵活性,又拥有FPGA并行处理的能力。本系统的设计充分发挥了Zynq芯片的优势,在软硬件划分上, 通过ARM处理器来实现图像的采集;图像角点及边缘检测用FPGA来完成,即通过硬件加速提升系统的整体性能。ARM处理器与FPGA通过AXI4总线进行数据交互,在Zynq上实现集图像采集、图像特征提取、图像显示为一体的片上系统。最终系统测试结果表明,采用硬件加速实现图像特征提取的相关算法比在ARM处理器软件上实现的算法的速度提高了6~8倍。

关键词: Zynq,FPGA,特征提取,软硬件协同设计

Abstract: Based on Zynq chip,the whole system is implemented with hardware and software co-design method.ARM+FPGA is used as internal architecture of the Zynq,so it has both of their advantages,which are flexibility of ARM and parallel processing capability of FPGA.The two advantages are fully used in this system.Image acquisition is implemented by ARM software design.Corner and edge detection is implemented by FPGA hardware design.The ARM processor and FPGA did the data interaction through bus AX14.This system has 3 main functions,which are image acquisition,image feature extraction and image display.The results show that this system implemented by Zynq,which used hardware acceleration algorithm,is 6 to 8 times faster than that of implemented only by ARM processor.

Key words: Zynq,FPGA,Feature extraction,Hardware and software co-design

[1] 陈军.基于ARM-Linux的嵌入式产品平台构建[D].杭州:浙江大学,2004.
[2] 唐敏.基于边缘和角点的特征提取方法与应用研究[D].长沙:国防科技大学,2006.
[3] 陈勇.基于机器视觉的表面缺陷检测系统的算法研究及软件设计[D].天津:天津大学,2006.
[4] 陈然.工业相机的自动调焦与图像拼接研究及在桥梁检测机器人中的应用[D].广州:华南理工大学,2014.
[5] 王阿妮,马彩文,晃长征,等.基于边缘相关的红外与可见光图像配准方法[J].现代电子技术,2009,2(10):104-106.
[6] 尚春红,赵明昌.复杂背景图像中军用靶子识别算法研究[J].计算机应用,2008,28(5):1257-1260.
[7] 宋余庆.数字医学图像[M].北京:清华大学出版社,2008.
[8] ARBELAEZ P,MAIRE M,FOWLKES C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,33(5):898-916.
[9] SCHAPIRE R E.The boosting approach to machine learning:an overview[C]∥MSRI Workshop on Nonlinear Estimation and Classification.Berkeley,CA,2002:312-318.
[10] 薛金龙.基于角点的图像特征提取与匹配算法研究[D].大连:大连理工大学,2014.
[11] ROSTEN E,DRUMMOND T.Machine learning for high speed corner detection[C]∥9th European Conference on Computer Vision.2006:430-443.
[12] 陆启帅,陆彦婷,王地.SoC与嵌入式Linux设计实战指南:兼容ARM Cortex-A9的设计方法[M].北京:清华大学出版社,2014:274-278.
[13] 杨霞.基于OMAP3530以太网视频采集系统的设计与实现[D].南京:南京邮电大学,2011.

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