Real-time robust object detection using an adjacent feature fusion-based single shot multibox detector

被引：1

作者：

Kim D. ^{[1
]}

Park S. ^{[1
]}

Kang D. ^{[1
]}

Paik J. ^{[1
]}

机构：

[1] Graduate School of Advanced Imaging Science,Multimedia,and Film, Chung-Ang University, Seoul

来源：

IEIE Transactions on Smart Processing and Computing | 2020年 / 9卷 / 01期

关键词：

Feature pyramid; Object detection; Pascal VOC; SSD;

D O I：

10.5573/IEIESPC.2020.9.1.022

中图分类号：

学科分类号：

摘要：

A single shot multibox detector (SSD) is used as a baseline for many object detection networks, since it can provide sufficiently high accuracy in real time. However, it cannot deal with objects of various sizes, because features used in an SSD are not robust to multi-scale objects. To solve this problem, we present an improved feature pyramid for using multi-scale context information. The proposed feature pyramid fuses only adjacent features of the conventional SSD to achieve high accuracy without decreasing the processing speed. Our detector, with a 320×320 input, achieved 79.1% mean average precision (mAP) at 63 frames per second on a Pascal Visual Object Classes Challenge 2007 test set using a single Nvidia 1080 Ti graphics processing unit. This result shows better performance than existing SSDs. © 2020 Institute of Electronics and Information Engineers. All rights reserved.

引用

页码：22 / 27

页数：5

共 16 条

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