Weighted Feature Pyramid Network for One-Stage Object Detection

摘要

One-stage object detection methods have attracted much attention for their high speed performance compared with two-stage methods. But one-stage methods under performs with small object detection. Feature Pyramid Network (FPN) was widely used to deal with this problem for its multi-scale feature present ability. However there still remains a few problems that are not considered in FPN, which results in limited improvement in detector performance. We note that FPN does not take the weight and scale distribution between different levels of feature maps into account when merging high-level feature maps and low-level feature maps. We present a network named Weighted Feature Pyramid Network (WFPN) to address these problems. Our experimental results on PASCAL VOC and MS COCO show that WFPN can significantly improve the detector performance, especially on small object detection.