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首页> 外国专利> Learning method and learning device for object detector with hardware optimization based on CNN for detection at distance or military purpose using image concatenation, and testing method and testing device using the same

Learning method and learning device for object detector with hardware optimization based on CNN for detection at distance or military purpose using image concatenation, and testing method and testing device using the same

机译：基于CNN的基于硬件的硬件优化对象探测器的学习方法和学习装置，以及利用图像级联进行远距离或军事目的的检测，以及使用该方法的测试设备

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摘要

A method for learning parameters of an object detector with hardware optimization based on a CNN for detection at distance or military purpose using an image concatenation is provided. The CNN can be redesigned when scales of objects change as a focal length or a resolution changes depending on the KPI. The method includes steps of: (a) concatenating n manipulated images which correspond to n target regions; (b) instructing an RPN to generate first to n-th object proposals in the n manipulated images by using an integrated feature map, and instructing a pooling layer to apply pooling operations to regions, corresponding to the first to the n-th object proposals, on the integrated feature map; and (c) instructing an FC loss layer to generate first to n-th FC losses by referring to the object detection information, outputted from an FC layer.
机译：提供了一种用于基于CNN的，通过硬件优化来学习物体检测器的参数的方法，以利用图像级联在远距离或军事目的上进行检测。当对象的比例随着焦距或分辨率的变化取决于KPI时，可以重新设计CNN。该方法包括以下步骤：（a）级联对应于n个目标区域的n个操纵图像; （b）通过使用集成特征图指示RPN在n个操纵图像中生成第一至第n个对象建议，并指示池化层对对应于第一至第n个对象建议的区域进行池化操作，在综合地图上; （c）通过参照从FC层输出的物体检测信息，指示FC损失层生成第1至第n FC损失。

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