  Due to the advantages of real-time detection and improved performance,
single-shot detectors have gained great attention recently. To solve the
complex scale variations, single-shot detectors make scale-aware predictions
based on multiple pyramid layers. However, the features in the pyramid are not
scale-aware enough, which limits the detection performance. Two common problems
in single-shot detectors caused by object scale variations can be observed: (1)
small objects are easily missed; (2) the salient part of a large object is
sometimes detected as an object. With this observation, we propose a new
Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid
features and explore scale-aware features. In NET, a Neighbor Erasing Module
(NEM) is designed to erase the salient features of large objects and emphasize
the features of small objects in shallow layers. A Neighbor Transferring Module
(NTM) is introduced to transfer the erased features and highlight large objects
in deep layers. With this mechanism, a single-shot network called NETNet is
constructed for scale-aware object detection. In addition, we propose to
aggregate nearest neighboring pyramid features to enhance our NET. NETNet
achieves 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MS
COCO dataset. As a result, NETNet achieves a better trade-off for real-time and
accurate object detection.
