A review of object detection based on deep learning

With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based methods, the deep learning-based object detection methods can learn both low-level and high-level im...

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Veröffentlicht in:Multimedia tools and applications Jg. 79; H. 33-34; S. 23729 - 23791
Hauptverfasser: Xiao, Youzi, Tian, Zhiqiang, Yu, Jiachen, Zhang, Yinshu, Liu, Shuai, Du, Shaoyi, Lan, Xuguang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2020
Springer Nature B.V
Schlagworte:
Algorithms
Artificial neural networks
Computer Communication Networks
Computer networks
Computer Science
Data Structures and Information Theory
Deep learning
Machine learning
Meteorological satellites
Multimedia Information Systems
Neural networks
Object recognition
Special Purpose and Application-Based Systems
Deep learning
Deep convolutional neural networks
Computer vision
Object detection
ISSN:1380-7501, 1573-7721
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Zusammenfassung:With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based methods, the deep learning-based object detection methods can learn both low-level and high-level image features. The image features learned through deep learning techniques are more representative than the handcrafted features. Therefore, this review paper focuses on the object detection algorithms based on deep convolutional neural networks, while the traditional object detection algorithms will be simply introduced as well. Through the review and analysis of deep learning-based object detection techniques in recent years, this work includes the following parts: backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications and future development directions. We hope this review paper will be helpful for researchers in the field of object detection.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08976-6