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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.