Adaptive region-aware feature enhancement for object detection

Abstract

Increasing object detectors reveal the importance of feature representation in improving detection performance. Currently, feature enhancement mainly focuses on Feature Pyramid Network (FPN) as well as Region-of-Interest (RoI) feature fusion in two-stage object detectors. Based on this, we propose Adaptive Region-aware Feature Enhancement method including Adaptive Region-aware FPN (AR-FPN) and Adaptive Region-aware RoI Feature Fusion (AR-RFF) modules. Specifically, AR-FPN aims to capture position-sensitive map for each level to enhance the pixel-wise interest degree and make the differences among levels more distinctive. AR-RFF focuses on obtaining distinguishable RoI features by introducing adaptive region information and eliminating scale inconsistency between the refined and original features. Extensive experiments show that our method acquires 1.7% AP higher at least and strong generalization capability compared to others.