...
首页> 外文期刊>International Journal of Agricultural and Biological Engineering >Kiwifruit recognition at nighttime using artificial lighting based on machine vision
【24h】

Kiwifruit recognition at nighttime using artificial lighting based on machine vision

机译:使用基于机器视觉的人工照明在夜间识别奇异果

获取原文
           

摘要

Most researches involved so far in kiwifruit harvesting robot suggest the scenario of harvesting in daytime for taking advantage of sunlight. A robot operating at nighttime can overcome the problem of low work efficiency and would help to minimize fruit damage. In addition, artificial lights can be used to ensure constant illumination instead of the variable natural sunlight for image capturing. This paper aims to study the kiwifruit recognition at nighttime using artificial lighting based on machine vision. Firstly, an RGB camera was placed underneath the canopy so that clusters of kiwifruits could be included in the images. Next, the images were segmented using an R-G color model. Finally, a group of image processing conventional methods, such as Canny operator were applied to detect the fruits. The image processing results showed that this capturing method could reduce the background noise and overcome any target overlapping. The experimental results showed that the optimal artificial lighting ranged approximately between 30-50 lx. The developed algorithm detected 88.3% of the fruits successfully. Keywords: Elliptic Hough transform, image capturing method, Kiwifruit, minimal bounding rectangle, optimal illumination intensity DOI: 10.3965/j.ijabe.20150804.1576 Citation: Fu L S, Wang B, Cui Y J, Su S, Gejima Y, Kobayashi T. Kiwifruit recognition at nighttime using artificial lighting based on machine vision. Int J Agric & Biol Eng, 2015; 8(4): 52-59.
机译:迄今为止,参与猕猴桃收获机器人的大多数研究都提出了在白天利用阳光进行收获的方案。夜间运行的机器人可以克服工作效率低的问题,并有助于最大程度地减少水果损坏。另外,可以使用人造光来确保恒定的照明,而不是使用可变的自然阳光进行图像捕获。本文旨在研究基于机器视觉的人工照明在夜间对猕猴桃的识别能力。首先,在树冠下放置一个RGB相机,以便将猕猴桃簇包括在图像中。接下来,使用R-G颜色模型对图像进行分割。最后,将一组图像处理常规方法(如Canny算子)应用于检测水果。图像处理结果表明,该捕获方法可以减少背景噪声并克服目标重叠。实验结果表明,最佳的人工照明范围约为30-50 lx。所开发的算法成功检测出88.3%的水果。关键字:椭圆霍夫变换,图像捕获方法,奇异果,最小边界矩形,最佳照明强度DOI:10.3965 / j.ijabe.20150804.1576引用:Fu LS,Wang B,Cui YJ,Su S,Gejima Y,Kobayashi T.奇异果识别在夜间使用基于机器视觉的人工照明。农业与生物工程学杂志,2015; 8(4):52-59。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号