Abstract
Scale problem lies in the heart of object detection. In this
work, we develop a novel Scale-Transferrable Detection
Network (STDN) for detecting multi-scale objects in images. In contrast to previous methods that simply combine
object predictions from multiple feature maps from different network depths, the proposed network is equipped with
embedded super-resolution layers (named as scale-transfer
layer/module in this work) to explicitly explore the interscale consistency nature across multiple detection scales.
Scale-transfer module naturally fits the base network with
little computational cost. This module is further integrated
with a dense convolutional network (DenseNet) to yield a
one-stage object detector. We evaluate our proposed architecture on PASCAL VOC 2007 and MS COCO benchmark
tasks and STDN obtains significant improvements over the
comparable state-of-the-art detection models