Detection of Young Green Apples in Orchard Environment Using Adaptive Ratio Chromatic Aberration and HOG-SVM

文献类型： 外文期刊

第一作者： Xia Xue

作者： Xia Xue;Zhou Guomin;Qiu Yun;Wang Jian;Hu Lin;Fan Jingchao;Guo Xiuming;Li Zhuang

作者机构：

关键词： Young fruit; Green apple; Chromatic aberration map; HOG-SVM

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, PT I

ISSN： 1868-4238

年卷期： 2019 年 545 卷

页码：

收录情况： SCI

摘要： It is still a challenge for fruit robot to automatic detecting young green apples in a complex grove environment due to color similarity with the background and varying illumination conditions. The purpose of this study was developing a robust method to detect young green apples in the tree canopy from low-cost color images acquired with diverse fruit sizes and under varying light circumstances. Adaptive green and blue chromatic aberration map was designed and combined with the iterative threshold segmentation algorithm to detect the region of interest contains potential apple fruits pixels. Then every potential fruit was identified by using an improved circular Hough transformation after morphological operation and blob analysis of the ITS outs which kept as many potential apple fruits pixels as possible. Finally, a kernel support vector machine classifier optimized by using grid search algorithm was built and combined with histogram of oriented gradients feature descriptor to distinguish and remove false fruit objects. The experimental result shows that the proposed method has better detection performance for young green apples.

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