基于模拟退火算法—支持向量机的储粮害虫识别分类

摘要:

将模拟退火算法应用于粮虫图像识别中支持向量机分类器参数C和g的优化，并与网格搜索法优化结果进行了对比，结果表明参数优化速度提高了3.91倍，分类器的识别率提高了5.56%。应用SAA—SVM分类器对粮仓中危害严重的9类粮虫进行了自动分类，识别率达到95.56%，证实了基于SAA—SVM的分类器对粮虫进行自动分类是可行的。

关键词:储粮害虫;识别;分类;模拟退火;支持向量机

Abstract:

The design of the classifier is an important part for the image recognition system of the stored-grain pests. The simulated annealing algorithm (SAA) was proposed to optimize parameters C and g in the classifier based on support vector machine (SVM), and it was compared with the grid-search optimization. The results indicated that the optimizing efficiency was improved about 3.91 times, and the recognition ratio of the SVM classifier was raised by 5.56%. The nine species of the stored-grain pests in grain-depot were automatically recognized by the classifier based on simulated annealing algorithm and support vector machine, the correct recognition ratio was over 95.56%. The experimental results prove that the method is practical and feasible.  

Key words:储粮害虫;识别;分类;模拟退火;支持向量机