A Convolutional Neural Network-Based Method for Small Traffic Sign Detection

被引：0
|
作者：
Zhou, Su [1 ]
Zhi, Xuelei [1 ]
Liu, Dong [1 ]
Ning, Hao [2 ]
Jiang, Lianxin [2 ]
Shi, Fanhuai [3 ]
机构：
[1] School of Automotive Studies, Tongji University, Shanghai,201804, China
[2] Suzhou Zhongdehongtai Electronic Polytron Technology Co. Ltd., Kunshan,215332, China
[3] College of Electronics and Information Engineering, Tongji University, Shanghai,201804, China
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2019年 / 47卷 / 11期
关键词：
Data set - Detection ability - Feature fusion - Small object detection - Small target detection - Small targets - Traffic sign detection;
D O I：
10.11908/j.issn.0253-374x.2019.11.012
中图分类号：
学科分类号：
摘要：
In order to solve the small target detection convolutional neural network algorithm, the PVANet convolutional neural network structure was improved to conduct the experiments of traffic sign detection on the TT100K traffic sign data set. The shallow feature extraction, deep feature extraction, and HyperNet multilayer feature fusion modules were improved. The experimental results show that the improved PVANet convolutional neural network has an excellent detection ability for small target objects, and the corresponding traffic sign detection algorithm can achieve a higher mAP (mean average precision). © 2019, Editorial Department of Journal of Tongji University. All right reserved.
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收藏
页码：1626 / 1632
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