Research on image recognition algorithm of citrus picking robot
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摘要: 柑橘作为我国重要的水果对增加我国果农收入有重要的作用，因此柑橘的自动采摘机器人已经成为当下研究的热点。针对柑橘采摘机器人核心的机器视觉部分进行柑橘图像识别算法的设计，选取HSV颜色空间中的S分量值作为柑橘识别的颜色特征，进行图像增强、去噪和分割预处理操作。综合比较Canny、Sobel、LOG三种边缘算子下的Hough变换，最终确定柑橘图像识别的最优方案。试验结果表明：基于Sobel边缘检测算子下的Hough变换的柑橘识别算法最优，对单个无枝叶遮挡柑橘识别成功率为97%,单个有枝叶遮挡柑橘识别成功率为90%,多个柑橘果实重叠识别成功率为80.6%,在一定程度上能够有效保证柑橘采摘机器人对柑橘果实的成功识别。Abstract: Citrus, as an important fruit in our country, plays an important role in increasing the income of our fruit farmers. Therefore, automatic robots of citrus picking have become a hot topic in recent research. A citrus image recognition algorithm was designed for the core machine vision part of the citrus picking robot. The S-component value in HSV color space was selected as the color feature for citrus recognition to perform image enhancement, denoising and segmentation pre-processing operations. By comprehensively comparing the Hough transform under the three edge operators Canny, Sobel and LOG, we finally determined the optimal solution for citrus image recognition. The experimental results show that the Hough transform based on the Sobel edge detection operator is the best, with a success rate of 97% for a single citrus without branch and leaf shading, 90% for a single citrus with branch and leaf shading, and 80.6% for multiple overlapping citrus.This algorithm can effectively guarantee the probability of successful identification of citrus to a certain extent.
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