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Using empirical evidence to generate synthetic training data for plant detection

机译:使用经验证据生成植物检测的合成训练数据

摘要

Implementations are described herein for automatically generating synthetic training images that are usable as training data for training machine learning models to detect, segment, and/or classify various types of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The digital image may depict the area under a lighting condition that existed in the area when a camera captured the digital image. Based at least in part on an agricultural history of the area, a plurality of three-dimensional synthetic plants may be generated. The synthetic training image may then be generated to depict the plurality of three-dimensional synthetic plants in the area. In some implementations, the generating may include graphically incorporating the plurality of three-dimensional synthetic plants with the digital image based on the lighting condition.
机译:本文描述了用于自动生成合成训练图像的实施方式,该训练图像可用作训练机器学习模型以检测,段和/或在数字图像中分类各种类型的植物。 在各种实施方式中,可以获得捕获区域的数字图像。 当相机捕获数字图像时,数字图像可以描绘在该区域中存在的照明条件下的区域。 至少部分地基于该地区的农业历史,可以产生多种三维合成植物。 然后可以产生合成训练图像以描绘该区域中的多个三维合成植物。 在一些实现中,生成可以包括以基于照明条件与数字图像一起地将多个三维合成植物结合在一起。

著录项

  • 公开/公告号US11113525B1

    专利类型

  • 公开/公告日2021-09-07

    原文格式PDF

  • 申请/专利权人 X DEVELOPMENT LLC;

    申请/专利号US202016877138

  • 发明设计人 LIANGHAO LI;KANGKANG WANG;ZHIQIANG YUAN;

    申请日2020-05-18

  • 分类号G06T15;G06K9;G06K9/62;G06N3/08;

  • 国家 US

  • 入库时间 2022-08-24 20:52:20

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