@Article{jai.2020.010193,
AUTHOR = {Chen Song, Xu Cheng, Yongxiang Gu, Beijing Chen, Zhangjie Fu},
TITLE = {A Review of Object Detectors in Deep Learning},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {59--77},
URL = {http://www.techscience.com/jai/v2n2/39515},
ISSN = {2579-003X},
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.},
DOI = {10.32604/jai.2020.010193}
}