Improving Object Detection with Feature Fusion Methods

Abstract

Facing the scale variation challenge in the topic of object detection, in this papers we design feature fusion methods to improve the representation ability of features. The proposed network, Feature Fusion Network (FFNet), contains mainly two parts: the Relationship Fusion Module (RFM) and the Numerical Fusion Module (NFM). The long dependencies information is shared within the pyramidal features in RFM. This information helps each feature emphasize informative regions and reduce the influence of useless regions. The NFM introduces the averaging operation to generate the fusion weights to retain the useful information and conduct the efficient feature fusion in the lateral connection. Finally, we conduct extensive experiments to verify the effects of our methods.