Deep Learning for Generic Object Detection: A Survey

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...

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Vydáno v:International journal of computer vision Ročník 128; číslo 2; s. 261 - 318
Hlavní autoři: Liu, Li, Ouyang, Wanli, Wang, Xiaogang, Fieguth, Paul, Chen, Jie, Liu, Xinwang, Pietikäinen, Matti
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2020
Springer
Springer Nature B.V
Témata:
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
Deep learning
Convolutional neural networks
Object recognition
Object detection
ISSN:0920-5691, 1573-1405
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-019-01247-4