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Easily deployable real-time detection method for small traffic signs

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成果类型:
期刊论文
作者:
Yaqin Li;Ziyi Zhang;Cao Yuan;Jing Hu
通讯作者:
Yuan, Cao
作者机构:
[Yaqin Li; Ziyi Zhang; Cao Yuan; Jing Hu] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
语种:
英文
关键词:
Small target;deep learning;model compression;traffic sign detection
期刊:
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
ISSN:
1064-1246
年:
2024
卷:
46
期:
4
页码:
8411-8424
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific ...

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