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An Overview of Deep Learning-Based Object Detection Methods

EasyChair Preprint no. 594

6 pagesDate: October 29, 2018


In recent years, there has been rapid development in the research area of deep learning. Deep learning was used to solve different problems, such as visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems.
In this paper we'll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, since the creation of AlexNet in 2012. Then, we'll make a comparison in terms of speed and accuracy between the most used state-of-the-art methods in object detection.

Keyphrases: Convolutional, Deep Learning Methods, neural networks, object detection

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Yassine Bouafia and Larbi Guezouli},
  title = {An Overview of Deep Learning-Based Object Detection Methods},
  howpublished = {EasyChair Preprint no. 594},

  year = {EasyChair, 2018}}
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