Sun Meili, Xu Liancheng, Luo Rong, Lu Yuqi, Jia Weikuan
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Front Plant Sci. 2022 Jun 9;13:864458. doi: 10.3389/fpls.2022.864458. eCollection 2022.
In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.
在青苹果收获或产量估计过程中，受果实颜色、光照和果园环境等因素影响，目标果实的准确识别和快速定位给视觉系统带来了巨大挑战。在本文中，我们借助深度图像的梯度场改进了一种用于RGB图像的密度峰值聚类分割算法，以定位和识别目标果实。具体而言，采用图像深度信息分析目标图像的梯度场，构建涡度中心和二维平面投影以实现精确的中心定位。接下来，应用优化的密度峰值聚类算法对目标图像进行分割，其中利用核密度估计优化分割算法，并应用双重排序算法高效获取目标图像的准确分割区域。最后，以圆心所在的分割区域为目标果实区域，采用最大值法确定半径。将上述两个结果合并以实现目标果实的轮廓拟合。该新方法无需迭代、分类器和多个样本，大大提高了操作效率。实验结果表明，所提方法显著提高了准确率和效率。同时，这种新方法值得进一步推广。
