  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.
