计算机科学
目标检测
水准点(测量)
管道(软件)
人工智能
对象(语法)
Viola–Jones对象检测框架
对象类检测
班级(哲学)
深度学习
多样性(控制论)
机器学习
点(几何)
集合(抽象数据类型)
视觉对象识别的认知神经科学
计算机视觉
特征提取
模式识别(心理学)
人脸检测
几何学
数学
面部识别系统
程序设计语言
地理
大地测量学
作者
Licheng Jiao,Fan Zhang,Fang Liu,Shuyuan Yang,Lingling Li,Zhixi Feng,Ronghai Qu
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:7: 128837-128868
被引量:819
标识
DOI:10.1109/access.2019.2939201
摘要
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.
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