  Object detection in images has reached unprecedented performances. The
state-of-the-art methods rely on deep architectures that extract salient
features and predict bounding boxes enclosing the objects of interest. These
methods essentially run on RGB images. However, the RGB images are often
compressed by the acquisition devices for storage purpose and transfer
efficiency. Hence, their decompression is required for object detectors. To
gain in efficiency, this paper proposes to take advantage of the compressed
representation of images to carry out object detection usable in constrained
resources conditions.
  Specifically, we focus on JPEG images and propose a thorough analysis of
detection architectures newly designed in regard of the peculiarities of the
JPEG norm. This leads to a $\times 1.7$ speed up in comparison with a standard
RGB-based architecture, while only reducing the detection performance by 5.5%.
Additionally, our empirical findings demonstrate that only part of the
compressed JPEG information, namely the luminance component, may be required to
match detection accuracy of the full input methods.
