SPCDet: Enhancing Object Detection with Combined Feature Fusing

Abstract

Feature pyramid and feature fusing are widely used in object detection. Using feature pyramid can confront the challenge of scale variation across different objects. Feature fusing imports context information to improve detection performance. Although detecting with feature pyramid and feature fusing has achieved some encouraging results, there are still some limitations owing to the features’ level variance among different layers. In this paper, we exploit that serial-parallel combined feature fusing can enhance object detection. Instead of detecting on the feature pyramid of backbone directly, we fuse different layers from backbone as base features. Then the base features are fed into a U-shape module to build local-global feature pyramid. At last, we use the pyramid to do the multi-scale detection with our combined feature fusing method. We call this one-stage detector SPCDet. It keeps real time speed and outperforms other detectors in trade-off between accuracy and speed.