  Almost all of the current top-performing object detection networks employ
region proposals to guide the search for object instances. State-of-the-art
region proposal methods usually need several thousand proposals to get high
recall, thus hurting the detection efficiency. Although the latest Region
Proposal Network method gets promising detection accuracy with several hundred
proposals, it still struggles in small-size object detection and precise
localization (e.g., large IoU thresholds), mainly due to the coarseness of its
feature maps. In this paper, we present a deep hierarchical network, namely
HyperNet, for handling region proposal generation and object detection jointly.
Our HyperNet is primarily based on an elaborately designed Hyper Feature which
aggregates hierarchical feature maps first and then compresses them into a
uniform space. The Hyper Features well incorporate deep but highly semantic,
intermediate but really complementary, and shallow but naturally
high-resolution features of the image, thus enabling us to construct HyperNet
by sharing them both in generating proposals and detecting objects via an
end-to-end joint training strategy. For the deep VGG16 model, our method
achieves completely leading recall and state-of-the-art object detection
accuracy on PASCAL VOC 2007 and 2012 using only 100 proposals per image. It
runs with a speed of 5 fps (including all steps) on a GPU, thus having the
potential for real-time processing.
