JCSE, vol. 16, no. 1, pp.14-24, 2022

DOI: http://dx.doi.org/10.5626/JCSE.2022.16.1.14

Improvement in Object Detection Using Multi-Scale RoI Pooling and Feature Pyramid Network

Seungtae Nam and Daeho Lee
Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Korea Department of Software Convergence, Kyung Hee University, Yongin, Korea

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.

Keyword: Region of interest pooling; Feature pyramid network; Object detection; Deep learning

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