屠 珺, 刘成良, 李彦明, 周 俊, 苑 进. 基于光照无关图的苹果图像识别方法[J]. 农业工程学报, 2010, 26(14): 26-31.
    引用本文: 屠 珺, 刘成良, 李彦明, 周 俊, 苑 进. 基于光照无关图的苹果图像识别方法[J]. 农业工程学报, 2010, 26(14): 26-31.
    Tu Jun, Liu Chengliang, Li Yanming, Zhou Jun, Yuan Jin. Apple recognition method based on illumination invariant graph[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(14): 26-31.
    Citation: Tu Jun, Liu Chengliang, Li Yanming, Zhou Jun, Yuan Jin. Apple recognition method based on illumination invariant graph[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(14): 26-31.

    基于光照无关图的苹果图像识别方法

    Apple recognition method based on illumination invariant graph

    • 摘要: 为了解决苹果采摘机器人的果实识别率在不同光照条件下表现不稳定的问题,该文提出一种基于光照无关图的苹果识别方法。该方法首先采用中值滤波法对苹果图像进行预处理,然后对处理后的彩色图像提取光照无关图,消除光照变化的影响,再采用Ostu阈值分割法进行目标果实的提取。最后通过对苹果图像进行识别试验的结果表明,在4种不同的光照情况下,采用基于光照无关图的识别方法得出的识别率的稳定度是不采用光照无关图的识别方法的3倍,并且其平均识别率也高达90.45%。基于光照无关图的苹果识别方法能够克服光照变化对目标识别带来的负面影响,完善室外环境果实识别技术。

       

      Abstract: To solve the problem that the recognition accuracy of apple-picking robots is unstable under varying illumination conditions, a novel method based on illumination invariant graph was proposed for apple recognition. Firstly, the algorithm of medium filtering was used for the pre-process of apple image. Then the illumination invariant graph was calculated to eliminate the influence of illumination change. Finally, Ostu method was used for recognizing apple fruit. The experiment result illustrated that the stability of recognition accuracy of the method based on illumination invariant graph was three times of the method without illumination invariant graph under four kinds of illumination conditions, and the average recognition accuracy was up to 90.45%. The apple recognition method based on illumination invariant graph can overcome the negative effects brought by illumination change and improve the fruit recognition technology in outdoor environment.

       

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