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.
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