基于深度卷积神经网络的目标检测研究综述

范丽丽; 赵浩宇; 胡黄水; 王振

doi:10.3788/OPE.20202805.1152

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基于深度卷积神经网络的目标检测研究综述

信息科学 | 更新时间：2020-07-09

- 基于深度卷积神经网络的目标检测研究综述
- Survey of target detection based on deep convolutional neural networks
- 光学精密工程 2020年28卷第5期页码：1152-1164
- 作者机构：
  
  1.吉林大学计算机科学与技术学院, 吉林长春 130012
  2.吉林大学符号计算与知识工程教育部重点实验室, 吉林长春 130012
  3.吉林大学学报(工学版)编辑部, 吉林长春 130012
  4.长春工业大学计算机科学与工程学院, 吉林长春 130012
  5.山东理工大学计算机科学与技术学院, 山东淄博 255000
- 作者简介：
  
  [ "范丽丽(1991-)，女，山东烟台人，博士研究生，2016年于吉林大学获得硕士学位，主要从事计算机视觉及人工智能方面的研究。E-mail：llfan18@mails.jlu.edu.cn" ]
  赵浩宇(1991-)，男，吉林长春人，硕士，2016年于吉林大学获得硕士学位，主要从事智能信息系统与科技信息传播方面的研究。通讯作者，E-mail: zhaohaoyu@jlu.edu.cn ZHAO Hao-yu, E-mail: zhaohaoyu@jlu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61841602);吉林省省级科技创新专项资助项目(20190302026GX);吉林省高等教育学会高教科研项目资助(JGJX2018D10);中央高校基本科研业务基金资助项目(JLU)
- DOI：10.3788/OPE.20202805.1152
  中图分类号： TP391
- 收稿日期：2019-10-30，
  
  修回日期：2019-12-27，
  
  录用日期：2019-12-27，
  
  纸质出版日期：2020-05-25
- 稿件说明：
移动端阅览
范丽丽, 赵浩宇, 胡黄水, 等. 基于深度卷积神经网络的目标检测研究综述[J]. 光学精密工程, 2020,28(5):1152-1164.

Li-li FAN, Hao-yu ZHAO, Huang-shui HU, et al. Survey of target detection based on deep convolutional neural networks[J]. Optics and precision engineering, 2020, 28(5): 1152-1164.
范丽丽, 赵浩宇, 胡黄水, 等. 基于深度卷积神经网络的目标检测研究综述[J]. 光学精密工程, 2020,28(5):1152-1164. DOI： 10.3788/OPE.20202805.1152.

Li-li FAN, Hao-yu ZHAO, Huang-shui HU, et al. Survey of target detection based on deep convolutional neural networks[J]. Optics and precision engineering, 2020, 28(5): 1152-1164. DOI： 10.3788/OPE.20202805.1152.

摘要

作为计算机视觉中的基本视觉识别问题，目标检测在过去的几十年中得到了广泛地研究。目标检测旨在给定图像中找到具有准确定位的特定对象，并为每个对象分配一个对应的标签。近年来，深度卷积神经网络DCNN(Deep Convolutional Neural Networks)凭借其特征学习和迁移学习的强大能力在图像分类方面取得了一系列突破，在目标检测方面，它越来越受到人们的重视。因此，如何将CNN应用于目标检测并获得更好的性能是一项重要的研究。首先回顾和介绍了几类经典的目标检测算法；然后将深度学习算法的产生过程作为切入点，以系统的方式全面概述了各种目标检测方法；最后针对目标检测和深度学习算法面临的重大挑战，讨论了一些未来的方向，以促进深度学习对目标检测的研究。

Abstract

Object detection

which is a fundamental visual recognition problem in computer vision

has been extensively studied in the past few decades and has become one of the popular research areas in the world. The aim of object detection is to accurately locate specific objects in a given image and assign a corresponding label to each object. In recent years

Deep Convolutional Neural Networks (DCNN) have been used in a series of developments in object detection and image classification owing to their powerful capabilities of feature learning and transfer learning.It has garnered considerable attention in the field of computer vision for object detection. Therefore

the method of applying CNN in target detection to obtain better performance is an important topic for research.First

we reviewed and introduced several types of classic object detection algorithms.Next

we considered the generation process of the deep learning algorithm as a starting point

analyzed the technical ideas and key problems of DCNN in the application of target detection

and provided a comprehensive overview of various target detection methods in a systematic manner. Finally

in view of the major challenges in target detection and deep learning algorithms

we provided future development scope and direction to promote the study of target detection using deep learning.

关键词

Keywords

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