北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (2): 333-339.doi: 10.13700/j.bh.1001-5965.2018.0313

• 论文 • 上一篇    下一篇

一种图像缩放算法的SoC协同加速设计方法

王鹏1, 曹云峰1, 许蕾1, 丁萌2, 张洲宇1, 曲金秋1   

  1. 1. 南京航空航天大学 航天学院, 南京 210016;
    2. 南京航空航天大学 民航学院, 南京 211106
  • 收稿日期:2018-05-30 出版日期:2019-02-20 发布日期:2019-03-04
  • 通讯作者: 曹云峰 E-mail:cyfac@nuaa.edu.cn
  • 作者简介:王鹏男,硕士研究生。主要研究方向:嵌入式、计算机视觉、无人机先进控制;曹云峰男,硕士,教授,博士生导师。主要研究方向:无人机飞行控制与导航、机器视觉与人工智能、基于模型的系统设计技术;许蕾男,硕士,讲师。主要研究方向:嵌入式、无人机智能控制;丁萌男,博士,副教授,硕士生导师。主要研究方向:计算机视觉、无人机导航、制导与控制。
  • 基金资助:
    国家自然科学基金(61673211);南京航空航天大学博士学位论文创新与创优基金(BCXJ18-11);中央高校基本科研业务费专项资金(kfjj20171502)

SoC collaborative acceleration design method for image scaling algorithm

WANG Peng1, CAO Yunfeng1, XU Lei1, DING Meng2, ZHANG Zhouyu1, QU Jinqiu1   

  1. 1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2018-05-30 Online:2019-02-20 Published:2019-03-04
  • Supported by:
    National Natural Science Foundation of China (61673211); Fundation of Innovation and Excellence Fund for Doctoral Dissertations in NUAA (BCXJ18-11); the Fundamental Research Funds for the Central Universities (kfjj20171502)

摘要: 针对无人机自主着陆的跑道检测、识别、跟踪等视觉算法中需要对大量图像进行缩放处理以便后续计算,但又对实时性要求比较高的情况,根据输入输出像素点的映射关系提出了一种适用于硬件加速的图像缩放算法,简化算法结构的同时利用现场可编程门阵列进行模块硬件功能的设计对算法加速,并采用软硬件协同的体系结构搭建实时图像处理系统。实验结果表明,该缩放算法处理精度高、耗时少,且用硬件逻辑实现后,可以进一步提速171倍,硬化后的系统可以通过摄像头获取图像数据,实时处理后在显示器中显示,达到30帧/s的处理速度,可以应用于实时性要求较高的图像处理算法中。

关键词: 图像缩放, 实时图像处理, 片上系统(SoC), 软硬件协同, 无人机视觉

Abstract: Aimed at the problem that a large number of images need to be scaled in the visual algorithm for the runway detection, recognition and tracking of the unmanned aerial vehicle with high real-time requirement, a new image scaling algorithm suitable for hardware acceleration is proposed based on the mapping relation of the input-output pixel. By simplifying the algorithm structure and using the field programmable gate array to design the hardware function of the module, the algorithm accelerates, and the real-time image processing system is built by the software and hardware cooperative architecture. The experimental results show that the improved scaling algorithm has high precision and less time consumption, and it can speed up by 171 times with the hardware logic. The hardened system can get the image data through the camera, and the real-time processing is displayed in the monitor, which has 30 frame/s processing speed. It can be applied to the image processing algorithm with high real-time requirement.

Key words: image scaling, real-time image processing, system on chip (SoC), software and hardware collaboration, unmanned aerial vehicle vision

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