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Due to the advantages of real-time detection and improved performance,single-shot detectors have gained great attention recently. To solve thecomplex scale variations, single-shot detectors make scale-aware predictionsbased on multiple pyramid layers. However, the features in the pyramid are notscale-aware enough, which limits the detection performance. Two common problemsin 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 issometimes detected as an object. With this observation, we propose a newNeighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramidfeatures and explore scale-aware features. In NET, a Neighbor Erasing Module(NEM) is designed to erase the salient features of large objects and emphasizethe features of small objects in shallow layers. A Neighbor Transferring Module(NTM) is introduced to transfer the erased features and highlight large objectsin deep layers. With this mechanism, a single-shot network called NETNet isconstructed for scale-aware object detection. In addition, we propose toaggregate nearest neighboring pyramid features to enhance our NET. NETNetachieves 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MSCOCO dataset. As a result, NETNet achieves a better trade-off for real-time andaccurate object detection.

NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection