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The Recognition of Overlapping Apple Fruits Based on Boundary Curvature Estimation

机译:基于边界曲率估计的重叠苹果果实识别

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In apple harvesting robot, the first key part is the machine vision system. Identifying single objects from fruit images is the first and foremost task in machine vision system. However, the main problem affecting the identification of single fruits is that fruit regions in image taken in unstructured orchard environment are overlapping in some cases. On the basis of studies on fruit image segmentation and boundary tracking, this paper proposed a new method for partition and recognition of overlapping apples based on boundary curvature. After the whole one-pixel boundary of overlapping fruits was extracted, the curvature at every point on the boundary was calculated. Then the whole boundary was split into segments by removing points with abrupt change in curvature and valid segments were retained through a screening process based on three criterion. At last, the identification of fruits was achieved by circle fitting and merging. Ultimately, the experimental results show that the proposed method is effective.
机译:在苹果收获机器人中,第一个关键部分是机器视觉系统。从水果图像中识别单个对象是机器视觉系统中的首要任务。然而,影响单果鉴定的主要问题是在非结构化果园环境中拍摄的图像中的水果区域在某些情况下是重叠的。在对水果图像分割和边界跟踪进行研究的基础上,提出了一种基于边界曲率的重叠苹果分区与识别的新方法。提取重叠水果的整个一像素边界后,计算边界上每个点的曲率。然后,通过去除曲率突然变化的点将整个边界划分为多个部分,并通过基于三个标准的筛选过程保留有效的部分。最后,通过圆拟合和融合实现了水果的识别。最终,实验结果表明该方法是有效的。

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