Open Access
REVIEW
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: .
Journal on Artificial Intelligence 2020, 2(2), 59-77. https://doi.org/10.32604/jai.2020.010193
Received 16 February 2020; Accepted 18 April 2020; Issue published 15 July 2020
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.
Keywords
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