Abstract
The feature pyramid network (FPN) enhances the localization accuracy and detection performance of small objects using multiple scales of the features. FPN adopts lateral connections and a top-down pathway to make low-level features semantically more meaningful. However, it uses only single-scale features to pool regions of interest (RoIs) when detecting objects. In this study, we showed that single-scale RoI pooling may not be the best solution for accurate localization and proposed multi-scale RoI pooling to improve the minor drawbacks of the FPN. The proposed method pools RoIs from three feature levels and concatenates the pooled features to detect objects. Thus, the FPN with multi-scale RoI pooling, called FPN+, detects objects by taking into account all information scattered across three feature levels. FPN+ improved the FPN by 2.81 and 1.1 points in COCO-style average precision (AP) when tested on PASCAL VOC 2007 test and COCO 2017 validation datasets, respectively.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
