摘要
谷物粒型是决定谷粒品质和产量的重要参数之一。传统人工测量粒型的方法耗时、工作量大、主观性强。本文首先介绍一种基于线阵列采集技术和工业输送技术的谷物粒型自动测量系统。为提高系统测量效率,文章中应用了图形处理器(GPU)并行处理技术,在统一计算设备架构(CUDA)下对测量算法进行优化。实验结果表明,基于GPU的并行加速算法,能有效提高测量效率,当图像中谷粒数近2000颗时,优化后的算法速度为中央处理器(CPU)下算法运行速度的400多倍,且随着采集图像中谷粒数的增多,优化测量算法的加速效果更显著。
Grain shape is an important feature, which determines the grain quality and yield. Traditional manual determination of grain shape is timeconsuming, laborious and subjective. A system for automatically determining the grain shape is presented based on a line-scan camera and a coveyor belt. In order to improve the grain shape determination efficiency, parallel processing technique based on Graphics Processing Unit （GPU） is used. On the basis of Compute Unified Device Architecture （CUDA）, the grain shape determination algorithm is optimized. The experiment result shows that the GPU-based accelerated algorithm can achieve a good effect on the measurement efficiency. When the number of grain in an image is approximately 2 000, the optimized algorithm gets a speedup of more than 400 times Moreover, as the quantity of kernels in the images gets larger, the performance of the grain shape determination algorithm is improved more significantly.
出处
《光电工程》
CAS
CSCD
北大核心
2012年第3期66-71,共6页
Opto-Electronic Engineering
基金
湖北省国家自然科学基金重点项目资助(2008CDA087)
关键词
谷物粒型
图形处理器
并行处理技术
加速算法
grain shape
graphics processing unit
parallel processing
acceleration algorithm
