首页> 外国专利> Methods and Software for Detecting Objects in an Image Using Contextual Multiscale Fast Region-Based Convolutional Neural Network

Methods and Software for Detecting Objects in an Image Using Contextual Multiscale Fast Region-Based Convolutional Neural Network

机译:基于上下文多尺度快速区域卷积神经网络的图像对象检测方法和软件

摘要

Methods of detecting an object in an image using a convolutional neural-network-based architecture that processes multiple feature maps of differing scales from differing convolution layers within a convolutional network to create a regional-proposal bounding box. The bounding box is projected back to the feature maps of the individual convolution layers to obtain a set of regions of interest (ROIs) and a corresponding set of context regions that provide additional context for the ROIs. These ROIs and context regions are processed to create a confidence score representing a confidence that the object detected in the bounding box is the desired object. These processes allow the method to utilize deep features encoded in both the global and the local representation for object regions, allowing the method to robustly deal with challenges in the problem of object detection. Software for executing the disclosed methods within an object-detection system is also disclosed.
机译:使用基于卷积神经网络的体系结构检测图像中对象的方法,该体系结构处理卷积网络中来自不同卷积层的不同比例的多个特征图,以创建区域建议边界框。将边界框投影回各个卷积层的特征图,以获得一组感兴趣区域(ROI)和相应的一组上下文区域,这些上下文区域为ROI提供了其他上下文。处理这些ROI和上下文区域以创建置信度分数,该置信度分数表示在边界框中检测到的对象是所需对象的置信度。这些过程允许该方法利用在全局和局部表示中编码的深度特征用于对象区域,从而使该方法能够可靠地应对对象检测问题。还公开了用于在物体检测系统内执行所公开的方法的软件。

著录项

  • 公开/公告号US2018068198A1

    专利类型

  • 公开/公告日2018-03-08

    原文格式PDF

  • 申请/专利权人 CARNEGIE MELLON UNIVERSITY;

    申请/专利号US201715697015

  • 发明设计人 MARIOS SAVVIDES;KHOA LUU;CHENCHEN ZHU;

    申请日2017-09-06

  • 分类号G06K9/32;G06K9/62;G06N3/04;

  • 国家 US

  • 入库时间 2022-08-21 12:59:07

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