基于深度学习的目标检测算法综述  

A Review of the Object Detection Algorithm Based on Deep Learning

作　　者：姚文清 李盛[1] 王元阳 YAO Wenqing;LI Sheng;WANG Yuanyang(Xijing University,Xi'an,Shaanxi Province,710123 China)

机构地区：[1]西京学院,陕西西安710123

出　　处：《科技资讯》2023年第16期185-188,共4页Science & Technology Information

基　　金：国家自然科学基金项目“女性语音产生机制差异的空气动力学建模对比研究”(项目编号:11974289)。

摘　　要：目标检测的任务是找出图像中所有感兴趣的目标(物体),确定它们的位置和类别。由于各类物体有不同的外观、形状和姿态,加上成像时光照、遮挡等因素的干扰,使目标检测成为计算机视觉领域中最具有挑战性的问题之一。该文综述了深度学习在目标检测方面有代表性算法的进展与展望。针对基于候选窗口(Region Proposal)的Two-Stage检测框架和基于回归的One-Stage检测框架,分别对有代表性的检测算法进行重点介绍,做出对比与总结;最后讨论目标检测领域存在的困难与挑战,并对未来目标检测方向的发展趋势进行展望。The task of object detection is to find out all the targets(objects)of interest in the image,and determine their location and category.Due to the different appearances,shapes and postures of various objects and the interference of lighting,occlusion and other factors during imaging,object detection becomes one of the most challenging problems in the field of computer vision.This paper reviews the progress and prospect of the representative algorithms of deep learning in object detection,introduces representative detection algorithms in detail for the Two-Stage detection framework based on the Region Proposal and the One-Stage detection framework based on regression,and compares and summarizes them.Finally,it discusses the difficulties and challenges in the field of target detection,and looks forward to the development trend of the future target detection direction.

关 键 词：目标检测 计算机视觉 深度学习 检测框架 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]