Abstract

Almost all of the current top-performing object detectionnetworks employ region proposals to guide the search forobject instances. State-of-the-art region proposal methodsusually need several thousand proposals to get high recall, thus hurting the detection efficiency. Although the latestRegion Proposal Network method gets promising detection accuracy with several hundred proposals, it still strugglesin 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 Featurewhich 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.