Fast Location and Recognition of Green Apple Based on RGB-D Image - Sun, Meili; Xu, Liancheng; Luo, Rong; Lu, Yuqi; Jia, Weikuan (Frontiers in Plant Science, 2022)
In the process of green apple harvesting or yield estimation, affected by factors such as fruit color, light, orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this paper, we improve a density peak cluster segmentation algorithm for RGB images with the help of gradient filed of depth images to locate and recognize target fruit. The image depth information is adopted to analyze the gradient field of the target image, at the same time vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Then a density peak clustering algorithm is applied to segment the target image, and the segmentation algorithm is optimized by kernel density estimation and a double sort algorithm to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the center of the circle is the target fruit area, and the maximum value method is used to determine the radius. The above two results are merged to achieve the contour fitting of the target fruit. The novel method is designed without iteration, classifier, and a number of samples, which has greatly improved on operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotions.