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Deep Learning for Generic Object Detection: A Survey

International journal of computer vision, 2020-02, Vol.128 (2), p.261-318 [Peer Reviewed Journal]

The Author(s) 2019 ;COPYRIGHT 2020 Springer ;International Journal of Computer Vision is a copyright of Springer, (2019). All Rights Reserved. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-019-01247-4

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Title:
Deep Learning for Generic Object Detection: A Survey
Author: Liu, Li ; Ouyang, Wanli ; Wang, Xiaogang ; Fieguth, Paul ; Chen, Jie ; Liu, Xinwang ; Pietikäinen, Matti
Subjects: Artificial Intelligence ; Computational linguistics ; Computer Imaging ; Computer Science ; Computer vision ; Deep learning ; Human-computer interaction ; Image Processing and Computer Vision ; Language processing ; Machine learning ; Machine vision ; Medical imaging equipment ; Natural language interfaces ; Object recognition ; Pattern Recognition ; Pattern Recognition and Graphics ; Representations ; Survey Papers ; Vision
Is Part Of: International journal of computer vision, 2020-02, Vol.128 (2), p.261-318
Description: 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.
Publisher: New York: Springer US
Language: English
Identifier: ISSN: 0920-5691
EISSN: 1573-1405
DOI: 10.1007/s11263-019-01247-4
Source: SpringerOpen
AUTh Library subscriptions: ProQuest Central

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Deep Learning for Generic Object Detection: A Survey

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