交通标志数据集用于农村地区的互联和自动化车辆操作
Mohammed Zakaria1, Tesfamichael Getahun1, Ali Karimoddini1
1North Carolina A&T State University,1601 East Market Street, Department of Electrical and Computer Engineering, Greensboro, NC 27411, US.
Data in brief
|December 11, 2025
概括
这项研究通过引入农村标志的新数据集来增强连接和自动化车辆 (CAV) 的交通信号识别. 增强数据集提高了识别精度,这对于更安全的农村自动驾驶至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 准确的交通信号识别对于连接和自动化车辆 (CAV) 的安全运行至关重要.
- 现有的数据集往往缺乏农村交通标志的表现,这限制了CAV在非城市环境中的性能.
- 农村环境面临着独特的标志性挑战,包括对动物和农业机械的警告.
研究的目的:
- 解决农村交通标志在现有数据集中的不足.
- 创建一个增强的LISA交通标志数据集,结合农村特有的标志.
- 评估增强数据集对CAV交通信号识别性能的影响.
主要方法:
- 通过添加特定农村标志来增强LISA交通标志数据集.
- 通过农村驾驶视频和谷歌地图街景收集数据.
- 使用基于YOLOv11的模型进行交通信号识别实验.
- 将数据集从47个扩展到55个类.
主要成果:
- 增强数据集提高了交通标志识别性能.
- 增强数据集的平均平均精度 (mAP) 为98.7%,与原始数据集的97.0%相比.
- 精度从95.5%提高到96.7%,并从95.1%提醒到97.5%.
结论:
- 增强数据集显著提高了农村CAV应用的交通标志识别.
- 这一数据集支持对各种环境的识别系统的开发和基准测试.
- 数据集的结构是为了在相关的智能交通研究中重复使用.
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