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Detection of red and bicoloured apples on tree with an RGB-D camera

机译:用RGB-D摄像机检测树上的红色和双色苹果

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摘要

Recognising and accurately locating fruits on a tree is a critical challenge in developing fruit-by-fruit robotic harvesting. Many researchers have investigated the potential of red, green, blue (RGB) colour imaging for this purpose, but have had limited success due to the occlusion of the target fruits by foliage, branches or other fruits as well as due to the nonuniform and unstructured nature of an orchard environment. Recently, novel, cost-effective camera systems have become available which provide both colour (RGB) and three dimensional (3D) shape information. As these have shown potential for 3D perception for robots operating in unstructured environments, the potential of such an RGB-D camera for the detection and localisation of red and bicoloured apples on tree was investigated in this study. Images were acquired with this camera system in fruit orchards under a light shield blocking direct sunlight, and an algorithm to detect and localise red and bicoloured apples based on colour and shape features was developed. When the algorithm was applied to the data acquired in these orchards, 100% of the fully visible apples and 82% of the partially occluded apples were detected correctly. The location estimation error was below 10 mm in all the coordinate axes of the Cartesian space. This high detection and location accuracy and short processing time (below 1 s for simultaneous detection of 20 apples), makes the developed algorithm suitable for implementation in a robotic harvesting system, and for yield estimation and orchard monitoring. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
机译:识别并准确地在树上定位水果是发展逐个水果的机器人收获的关键挑战。许多研究人员已经研究了为此目的进行红色,绿色,蓝色(RGB)彩色成像的潜力,但由于目标果实被叶子,树枝或其他果实阻塞,以及由于不均匀且不规则的结构,因此取得的成功有限果园环境的性质。近来,已经提供了新颖的,具有成本效益的相机系统,其既提供彩色(RGB)又提供三维(3D)形状信息。由于这些已经显示了在非结构化环境中操作机器人进行3D感知的潜力,因此在此研究中,研究了这种RGB-D相机用于检测和定位树上红色和双色苹果的潜力。使用此相机系统在果园中的遮光罩下遮挡了直射阳光,获取了图像,并开发了一种基于颜色和形状特征检测和定位红色和双色苹果的算法。当将该算法应用于在这些果园中获取的数据时,可以正确检测出100%的完全可见苹果和82%的部分遮挡苹果。在笛卡尔空间的所有坐标轴上,位置估计误差均在10 mm以下。这种高检测和定位精度以及较短的处理时间(不到1秒即可同时检测20个苹果),使该开发的算法适合在机器人收割系统中实施,并适于产量估算和果园监控。 (C)2016年。由Elsevier Ltd.出版。保留所有权利。

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