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A survey of image object detection algorithm based on deep learning
- Telecommunications Science Vol. 36, Issue 7, Pages: 92-106(2020)
- 作者机构：
1. 杭州电子科技大学，浙江 杭州 310018
2. 浙江宇视科技有限公司，浙江 杭州 310051
3. 之江实验室，浙江 杭州 311121
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.1000-0801.2020199
CLC： TP393-
Published Online：2020-07，
Published：20 July 2020
- 稿件说明：
移动端阅览
Tingting ZHANG, Jianwu ZHANG, Chunsheng GUO, et al. A survey of image object detection algorithm based on deep learning[J]. Telecommunications science, 2020, 36(7): 92-106.
Tingting ZHANG, Jianwu ZHANG, Chunsheng GUO, et al. A survey of image object detection algorithm based on deep learning[J]. Telecommunications science, 2020, 36(7): 92-106. DOI： 10.11959/j.issn.1000-0801.2020199.
摘要
图像目标检测是找出图像中感兴趣的目标，并确定他们的类别和位置，是当前计算机视觉领域的研究热点。近年来，由于深度学习在图像分类方面的准确度明显提高，基于深度学习的图像目标检测模型逐渐成为主流。首先介绍了图像目标检测模型中常用的卷积神经网络；然后，重点从候选区域、回归和anchor-free方法的角度对现有经典的图像目标检测模型进行综述；最后，根据在公共数据集上的检测结果分析模型的优势和缺点，总结了图像目标检测研究中存在的问题并对未来发展做出展望。
Abstract
Image object detection is to find out the objects of interest in the image and determine their classifications and locations.It is a research hotspot in the field of computer vision.In recent years
due to the significant improvement in the accuracy of image classification with deep learning
image object detection models based on deep learning have gradually became mainstream.Firstly
the convolutional neural networks commonly used in image object detection were briefly introduced.Then
the existing classical image object detection models were reviewed from the perspective of candidate regions
regression and anchor-free methods.Finally
according to the detection results on the public dataset
the advantages and disadvantages of the models were analyzed
the problems in the image object detection research were summarized and the future development was forecasted.
关键词
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references
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