Next Article in Journal
Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar
Previous Article in Journal
Proposal of a Decoupled Structure of Fuzzy-PID Controllers Applied to the Position Control in a Planar CDPR
Article

Object Detection Using Improved Bi-Directional Feature Pyramid Network

Department of Electronic Engineering, Inha University, Incheon 22212, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Yoichi Hayashi
Electronics 2021, 10(6), 746; https://doi.org/10.3390/electronics10060746
Received: 16 January 2021 / Revised: 16 March 2021 / Accepted: 16 March 2021 / Published: 22 March 2021
(This article belongs to the Section Artificial Intelligence)
Conventional single-stage object detectors have been able to efficiently detect objects of various sizes using a feature pyramid network. However, because they adopt a too simple manner of aggregating feature maps, they cannot avoid performance degradation due to information loss. To solve this problem, this paper proposes a new framework for single-stage object detection. The proposed aggregation scheme introduces two independent modules to extract global and local information. First, the global information extractor is designed so that each feature vector can reflect the information of the entire image through a non-local neural network (NLNN). Next, the local information extractor aggregates each feature map more effectively through the improved bi-directional network. The proposed method can achieve better performance than the existing single-stage object detection methods by providing improved feature maps to the detection heads. For example, the proposed method shows 1.6% higher average precision (AP) than the efficient featurized image pyramid network (EFIPNet) for the MicroSoft Common Objects in COntext (MS COCO) dataset. View Full-Text
Keywords: object detection; non-local neural network; feature pyramid network object detection; non-local neural network; feature pyramid network
Show Figures

Figure 1

MDPI and ACS Style

Quang, T.N.; Lee, S.; Song, B.C. Object Detection Using Improved Bi-Directional Feature Pyramid Network. Electronics 2021, 10, 746. https://doi.org/10.3390/electronics10060746

AMA Style

Quang TN, Lee S, Song BC. Object Detection Using Improved Bi-Directional Feature Pyramid Network. Electronics. 2021; 10(6):746. https://doi.org/10.3390/electronics10060746

Chicago/Turabian Style

Quang, Tran N., Seunghyun Lee, and Byung C. Song. 2021. "Object Detection Using Improved Bi-Directional Feature Pyramid Network" Electronics 10, no. 6: 746. https://doi.org/10.3390/electronics10060746

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop