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A Review of Object Detectors in Deep Learning

Chen Song1, Xu Cheng1, *, Yongxiang Gu1, Beijing Chen1, Zhangjie Fu1

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China.

* Corresponding Author: Xu Cheng. Email: email.

Journal on Artificial Intelligence 2020, 2(2), 59-77. https://doi.org/10.32604/jai.2020.010193

Abstract

Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based on deep learning and state-of-the-art detection results for object detectors. Finally, we discuss several promising directions for future research.

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Cite This Article

C. Song, X. Cheng, Y. Gu, B. Chen and Z. Fu, "A review of object detectors in deep learning," Journal on Artificial Intelligence, vol. 2, no.2, pp. 59–77, 2020. https://doi.org/10.32604/jai.2020.010193



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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