Deep learning for generic object detection : a survey
|Author:||Liu, Li1,2; Ouyang, Wanli3; Wang, Xiaogang4;|
1National University of Defense Technology, Changsha, China
2University of Oulu, Oulu, Finland
3University of Sydney, Camperdown, Australia
4Chinese University of Hong Kong, Sha Tin, China
5University of Waterloo, Waterloo, Canada
|Online Access:||PDF Full Text (PDF, 7.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001131851
|Publish Date:|| 2020-01-13
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
International journal of computer vision
|Pages:||261 - 318|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
Open access funding provided by University of Oulu including Oulu University Hospital. The authors would like to thank the pioneering researchers in generic object detection and other related fields. The authors would also like to express their sincere appreciation to Professor Jiří Matas, the associate editor and the anonymous reviewers for their comments and suggestions. This work has been supported by the Center for Machine Vision and Signal Analysis at the University of Oulu (Finland) and the National Natural Science Foundation of China under Grant 61872379.
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.