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STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments

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成果类型:
期刊论文
作者:
Lai, Huaqing;Chen, Liangyan;Liu, Weihua;Yan, Zi;Ye, Sheng
通讯作者:
Chen, LY
作者机构:
[Chen, Liangyan; Chen, LY; Liu, Weihua; Ye, Sheng; Yan, Zi; Lai, Huaqing] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
通讯机构:
[Chen, LY ] W
Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
small object detection;multi-scale feature fusion;loss function;data augmentation;K-means plus plus
期刊:
Sensors
ISSN:
1424-3210
年:
2023
卷:
23
期:
11
页码:
5307-
基金类别:
This work was supported by excellent young and middle-aged scientific and technological innovation teams in Colleges and universities of Hubei Province under Grant T2021009.
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
摘要:
The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOL...

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