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A review of object detection based on deep learning

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Abstract

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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Acknowledgments

This work was supported in part by NSFC under grant No. 61876148, No. 61866022, and No. 61703328. This work was also supported in part by the key project of Trico-Robot plan of NSFC under grant No. 91748208, key project of Shaanxi province No.2018ZDCXL-GY-06-07, the Fundamental Research Funds for the Central Universities No. XJJ2018254, and China Postdoctoral Science Foundation NO. 2018M631164.

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Youzi Xiao, Zhiqiang Tian, Jiachen Yu, Yinshu Zhang & Shuai Liu

  2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China

    Shaoyi Du & Xuguang Lan

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  1. Youzi Xiao
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  2. Zhiqiang Tian
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  3. Jiachen Yu
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  4. Yinshu Zhang
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  7. Xuguang Lan
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Correspondence to Zhiqiang Tian.

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Xiao, Y., Tian, Z., Yu, J. et al. A review of object detection based on deep learning. Multimed Tools Appl 79, 23729–23791 (2020). https://doi.org/10.1007/s11042-020-08976-6

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