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Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy

文献类型： 外文期刊

作者： Wu, Gang 1 ; Li, Bin 2 ; Zhu, Qibing 1 ; Huang, Min 1 ; Guo, Ya 1 ;

作者机构： 1.Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, 11 Shuguang Garden Middle Rd, Beijing 100097, Peoples R China

关键词： Fruit recognition; Point cloud; Geometry feature; Local descriptor; Global descriptor

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2020 年 174 卷

页码：

收录情况： SCI

摘要： Fruit image segmentation is an essential step to distinguish fruits from the background. In order to improve the fruits recognition accuracy for harvesting robots in three-dimensional (3D) space, a method with the fusion of color and 3D geometry features for fruit point cloud segmentation was proposed in this study. The local descriptor was applied to obtain the candidate regions, and the global descriptor was used to obtain the final segmented results. Firstly, the hue, saturation, value (HSV) color features and normal orientation features of pixels were fused to obtain the preliminary segmentation results. Then, the pre-processed color image and depth image were converted to a point cloud, and it was clustered into multiple regions by the Euclidean clustering algorithm. Finally, we utilized the viewpoint feature histogram (VFH) of each point cloud cluster to remove the remaining non-fruit regions. The experiments showed that the segmentation accuracy of the proposed method was 98.99%, and the precision was 80.09%, which are both superior to the traditional color segmentation methods. In addition, a fruit detection method based on shape analysis showed that it is more effective in improving fruit recognition rate and reducing false detection rate than the color segmentation methods.

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