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Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Shivang Agarwal 1 Jean Ogier Du Terrail 1, 2 Frédéric Jurie 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.
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Preprints, Working Papers, ...
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Contributor : Jean Ogier Du Terrail <>
Submitted on : Friday, August 16, 2019 - 6:02:26 AM
Last modification on : Tuesday, August 20, 2019 - 1:59:07 PM
Long-term archiving on: : Wednesday, January 8, 2020 - 2:50:12 PM


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  • HAL Id : hal-01869779, version 2
  • ARXIV : 1809.03193


Shivang Agarwal, Jean Ogier Du Terrail, Frédéric Jurie. Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks. 2019. ⟨hal-01869779v2⟩



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