Accurate positioning of fruit is a key issue that has attracted much attention in the field of harvest robots. The complex environment and close proximity make the perception of dense crops in greenhouses a challenging problem. Different from various solutions proposed involving special equipment or other auxiliary information, we propose a novel positioning approach based on instance segmentation using a monocular RGB camera. To achieve high position accuracy, we first design a deep convolutional neural network (CNN) in a multi-task framework to export a binary segmentation map and an embedded feature map. To solve the problem of performance degradation in the intersection-over-union (IoU) for the binary segmentation task caused by multi-task optimisation, the encoder part of our network is redesigned on the basis of a Visual Geometry Group network with 16 convolutional layers (VGG-16). Then, mean-shift clustering is used to achieve instance segmentation. Finally, a contour-finding algorithm is presented for outlining fruit without the help of any contextual information. Based on these contours, the five fruits with the largest contour areas are selected as the targets for positioning. We verify our method on a public sweet pepper dataset and achieve competitive results. Divided by the radius of the fruit, the average position error for the first target in the harvesting order is 0.18, which shows that our method outperforms the semantic segmentation method. For the first five targets in the harvesting order, this index is less than 0.3 on average, similar to that of the semantic segmentation method with only one target output.

中文翻译：

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甜椒的单目定位：收获机器人的实例分割方法

水果的准确定位是收获机器人领域备受关注的关键问题。复杂的环境和近距离使温室中作物密集的感知成为一个具有挑战性的问题。与提出的涉及特殊设备或其他辅助信息的各种解决方案不同，我们提出了一种基于使用单目 RGB 相机的实例分割的新型定位方法。为了实现高定位精度，我们首先在多任务框架中设计了一个深度卷积神经网络 (CNN)，以导出二进制分割图和嵌入的特征图。为了解决多任务优化导致的二元分割任务的交集（IoU）性能下降问题，我们网络的编码器部分是在具有 16 个卷积层 (VGG-16) 的 Visual Geometry Group 网络的基础上重新设计的。然后，使用均值漂移聚类来实现实例分割。最后，提出了一种轮廓查找算法，用于在没有任何上下文信息帮助的情况下勾勒水果。根据这些轮廓，选择轮廓面积最大的五个水果作为定位目标。我们在公共甜椒数据集上验证了我们的方法并取得了有竞争力的结果。除以水果的半径，收获顺序中第一个目标的平均位置误差为 0.18，这表明我们的方法优于语义分割方法。对于收获顺序的前五个目标，该指数平均小于 0.3，