Weighted Feature Pyramid Networks for Object Detection
Authors:
- 1 December 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1500-1504
Abstract
Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.Keywords
This publication has 8 references indexed in Scilit:
- Panoptic Feature Pyramid Networks2019
- NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection2019
- Graph-based RGB-D Image Segmentation Using Color-directional-region Merging2019
- Feature Pyramid Networks for Object Detection2017
- You Only Look Once: Unified, Real-Time Object Detection2016
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation2014
- ORB: An efficient alternative to SIFT or SURF2011
- Histograms of Oriented Gradients for Human Detection2005