  Recent advances in deep learning have enabled complex real-world use cases
comprised of multiple vision tasks and detection tasks are being shifted to the
edge side as a pre-processing step of the entire workload. Since running a deep
model on resource-constraint devices is challenging, techniques for efficient
inference methods are demanded. In this paper, we present an objectness-aware
object detection method to reduce computational cost by sparsifying activation
values on background regions where target objects don't exist. Sparsified
activation can be exploited to increase inference speed by software or hardware
accelerated sparse convolution techniques. To accomplish this goal, we
incorporate a light-weight objectness mask generation (OMG) network in front of
an object detection (OD) network so that it can zero out unnecessary background
areas of an input image before being fed into the OD network. In experiments,
by switching background activation values to zero, the average number of zero
values increases further from 36% to 68% on MobileNetV2-SSDLite even with ReLU
activation while maintaining accuracy on MS-COCO. This result indicates that
the total MAC including both OMG and OD networks can be reduced to 62% of the
original OD model when only non-zero multiply-accumulate operations are
considered. Moreover, we show a similar tendency in heavy networks (VGG and
RetinaNet) and an additional dataset (PASCAL VOC).
