关键词: 机器人, 苹果幼果, 色差图, Blob分析, HOG特征, 支持向量机, 圆形Hough变换

Abstract: In order to realize  automatically managing fruit production by robot during young fruit period, this paper took young green apples in orchard as object and studied the detection method of young green apples by machine under natural environment. Firstly, adaptive green and blue chromatic aberration (AGBCA) map was designed and combined with the iterative threshold segmentation (ITS) algorithm  to detect region of interest (ROI) contained potential apple fruits pixels. Then, potential fruits were identified by an improved circular hough transformation (CHT) after morphological operation and Blob analysis of the results obtained from AGBCA and ITS, which kept  many potential apple fruits pixels as possible. Finally, a kernel support vector machine(SVM) classifier, optimized by grid search optimal algorithm, was built to remove false fruit objects based on histogram of oriented gradient(HOG) feature descriptor. The experimental results showed that the true positive rate of proposed method was 88.51%, false negative rate and false positive rate were 11.49% and 4.84%, respectively. And the F1-Measure of proposed model was 90.29%, indicating the proposed method had better detection ability and robustness for young green apples detection. The results provided references to fruit robot for automatic detection during young fruit stage.

Key words: robot, young green apple, chromatic aberration map, Blob analysis, HOG feature, SVM, CHT