  Channel pruning is one of the important methods for deep model compression.
Most of existing pruning methods mainly focus on classification. Few of them
conduct systematic research on object detection. However, object detection is
different from classification, which requires not only semantic information but
also localization information. In this paper, based on discrimination-aware
channel pruning (DCP) which is state-of-the-art pruning method for
classification, we propose a localization-aware auxiliary network to find out
the channels with key information for classification and regression so that we
can conduct channel pruning directly for object detection, which saves lots of
time and computing resources. In order to capture the localization information,
we first design the auxiliary network with a contextual ROIAlign layer which
can obtain precise localization information of the default boxes by pixel
alignment and enlarges the receptive fields of the default boxes when pruning
shallow layers. Then, we construct a loss function for object detection task
which tends to keep the channels that contain the key information for
classification and regression. Extensive experiments demonstrate the
effectiveness of our method. On MS COCO, we prune 70\% parameters of the SSD
based on ResNet-50 with modest accuracy drop, which outperforms
the-state-of-art method.
