报告详情
Vehicle Embedded Traffic Sign Recognition
编号：147
访问权限：仅限参会人
更新：2024-10-23 10:02:35 浏览：36次
张贴报告
报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
In response to the challenges encountered in traffic sign detection and recognition, such as small target size, variable shape, difficulty in feature extraction, and susceptibility to complex background, this study proposed a lightweight network model called SCF-YOLOv5. The algorithm integrates the channel attention mechanism before the SPPF layer of the backbone network of the YOLOv5 model, which enhances the model's ability to identify key information. In the neck network, a lightweight general up-sampling operator CARAFE was used instead of traditional up-sampling techniques, improving image resolution through content-aware technology. Furthermore, the CIOU loss function was optimized to Focal loss, effectively addressing class imbalance and sample imbalance issues. Finally, the algorithm was deployed on the Raspberry PI embedded platform. Compared with YOLOv5s, the number of parameters decreased by 40.25%, FPS increased by 11.04%, and mean average precision increased by 3.6%, which enhanced the accuracy and robustness of the algorithm.
关键词
deep learning, traffic sign recognition, yolov5 improvement, intelligent vehicle I.INTRODU
报告人
ZhangWenjie
student Hefei Normal University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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