首页> 外国专利> MODEL TRAINING METHOD AND SYSTEM FOR AUTOMATICALLY DETERMINING DAMAGE LEVEL OF EACH OF VEHICLE PARTS ON BASIS OF DEEP LEARNING

MODEL TRAINING METHOD AND SYSTEM FOR AUTOMATICALLY DETERMINING DAMAGE LEVEL OF EACH OF VEHICLE PARTS ON BASIS OF DEEP LEARNING

机译:模型训练方法和系统,用于在深度学习基础上自动确定每种车辆零件的损坏水平

摘要

The present invention relates to a method and a system for training a model for automatically determining the degree of damage for each vehicle area based on deep learning, which generate a model capable of quickly calculating a consistent and reliable vehicle repair quote by learning so as to automatically extract a picture in which it is possible to determine the degree of damage among accident vehicle pictures by using the Mask R-CNN framework and the Inception V4 network structure based on deep learning, and learning the degree of damage for each type of damage.
机译:本发明涉及一种用于训练模型的方法和系统,用于基于深度学习自动确定每个车辆区域的损坏程度,这产生了一种能够通过学习快速计算一致和可靠的车辆修复报价的模型 通过使用基于深度学习的掩模R-CNN框架和初始V4网络结构,自动提取一张图片,其中可以通过使用掩模R-CNN框架和初始V4网络结构来确定事故车辆图像之间的损坏程度,并学习每种类型损坏的损坏程度。

著录项

  • 公开/公告号US2021327040A1

    专利类型

  • 公开/公告日2021-10-21

    原文格式PDF

  • 申请/专利权人 AGILESODA INC.;

    申请/专利号US202117362120

  • 发明设计人 TAE YOUN KIM;JIN SOL EO;BYUNG SUN BAE;

    申请日2021-06-29

  • 分类号G06T7;G06N3/08;

  • 国家 US

  • 入库时间 2024-06-14 22:15:29

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