Feature Enhancement SSD for Object Detection

被引：0
|
作者：
Tan H. [1 ]
Li S. [1 ]
Liu B. [1 ]
Liu X. [1 ]
机构：
[1] School of Mathematical Sciences, Dalian University of Technology, Dalian
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 04期
关键词：
Feature fusion; Network structure; Object detection; Single shot multibox detector;
D O I：
10.3724/SP.J.1089.2019.17331
中图分类号：
学科分类号：
摘要：
This paper presents feature enhancement single shot multi-box detector (FE-SSD) for object detection. In FE-SSD network structure, firstly we apply scale-invariant convolution operation on each scale feature map in SSD's pyramid feature maps. Then fusing the original feature and convolved feature generates new SSD's feature pyramid, which will be fed to multibox detectors to predict the final detection results. On the PASCAL VOC2007 test, our network can achieve 78.0% mean average precision (mAP) at the speed of 82.5 frame per second (FPS) with the input size 300×300. On extended experiment, FE-SSD performance over SSD in blurry object detection. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：573 / 579
页数：6
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