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METHOD FOR SELECTING LEARNED MODEL, METHOD FOR GENERATE TRAINING DATA, METHOD FOR GENERATE LEARNED MODEL, COMPUTER AND PROGRAM

机译:学习模型的选择方法,训练数据的生成方法,学习模型的生成方法,计算机及程序

摘要

To provide a method for selecting a learned model having high accuracy and the like according to an object, in object detection.SOLUTION: A learned model related to machine learning of an image is selected. Each image has a possibility of including three kinds or more of objects. A computer 10 acquires correct answer data A in which each object was detected, about a plurality of images I3 for a correct answer including a plurality of correct answer images I5 for learning and a plurality of correct answer images I6 for verification, generates learned models M1 - M6 according to a kind set for each kind set of the object, generates data B1 - B6 for verification for each kind set, and selects a model which is the most accurate in the data B1 - B6 for verification of the learned models M1 - M6 according to the kind set, for each kind of the object.SELECTED DRAWING: Figure 9
机译:为了提供一种在物体检测中根据物体选择具有高精度等的学习模型的方法。解决方案:选择与图像的机器学习有关的学习模型。每个图像都可能包含三种或更多种对象。计算机10针对包括用于学习的多个正确答案图像I5和用于验证的多个正确答案图像I6的用于正确答案的多个图像I3,获取检测到每个对象的正确答案数据A,以生成学习模型M1。 -M6根据对象的每个种类组的种类组,生成用于每个种类组的验证的数据B1-B6,并选择数据B1-B6中最准确的模型以验证所学习的模型M1- M6根据种类设置,针对每种对象。选定的图:图9

著录项

  • 公开/公告号JP2019219728A

    专利类型

  • 公开/公告日2019-12-26

    原文格式PDF

  • 申请/专利权人 MITSUBISHI ELECTRIC INFORMATION SYSTEMS CORP;

    申请/专利号JP20180114544

  • 发明设计人 ISANO SHOTO;

    申请日2018-06-15

  • 分类号G06T7;G06N20;

  • 国家 JP

  • 入库时间 2022-08-21 11:32:44

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