Publication Type

Journal Article

Version

submittedVersion

Publication Date

1-2020

Abstract

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

Keywords

Deep convolutional neural networks, Deep learning, Object detection

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Neurocomputing

ISSN

0925-2312

Identifier

10.1016/j.neucom.2020.01.085

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.neucom.2020.01.085

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