A New Feature Pyramid Network for Object Detection

Abstract

Aiming at the problems of high computational cost based on deep backbones (e.g., ResNet-50, ResNet-101, DenseNet-169) in the state-of-the-art method about object detector, this paper improves the capability of feature representations by using New Feature Pyramid module on the basis of fast lightweight backbone network (vgg-16), and finally establishes a fast and accurate detector. The architecture of our model is named New FPN (New Feature Pyramid Network). Based on the structure of Feature Pyramid Network, we design a novel New Feature Pyramid Network, which consists of a combination of top-down and bottom-up connections to fuse features across scales, and achieves high-level semantic feature map at all scales. The experimental results show that New FPN achieves state-of-the-art detection accuracy (i.e. 79.2%mAP) on PASCAL VOC 2007 with high efficiency (i.e. 73FPS).