DORIE:道路基础设施元素数据集 - - 一个用于实时巡逻车辆监控的YOLO架构的基准
Iason Katsamenis1, Nikolaos Bakalos1, Andreas Lappas1
1Institute of Communications and Computer Systems, 9 Ir. Politechniou, 15773 Athens, Greece.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
一个新的数据集,DORIE,通过详细说明基础设施元素,如护和标志,提高了道路安全. 该资源有助于开发用于实时监控和维护的智能系统.
科学领域:
- 计算机视觉 计算机视觉
- 道路安全工程 道路安全工程
- 数据集创建 数据集创建
背景情况:
- 现有的驾驶数据集缺乏全面的道路基础设施元素表示.
- 关键的安全元素,如护,柱子和标志是不足的.
- 这种差距阻碍了先进的道路安全和维护系统的发展.
研究的目的:
- 介绍DORIE (道路基础设施元件数据集),用于基础设施监控的高分辨率数据集.
- 为道路基础设施的实时对象检测提供一个基准.
- 促进对智能道路安全和维护的研究.
主要方法:
- 策划了一个新的数据集 (DORIE),包含938个图像和超过6800个标注,分为10个类别.
- 在DORIE数据集上评估了YOLOv8,YOLOv11和YOLOv12物体检测模型.
- 在多个尺度和输入分辨率上评估性能,测量平均精度 (mAP) 和推断延迟.
主要成果:
- 更大的YOLO模型和更高的分辨率提高了mAP的40%,特别是在交通标志和柱子等小物体上.
- 推理延迟从5.7到245.2毫秒不等,突出了准确性和速度的权衡.
- 证明了YOLO模型用于检测道路基础设施元素的有效性.
结论:
- DORIE为推进道路基础设施监测研究提供了一个有价值的,可重复的资源.
- 数据集和YOLO基线支持开发智能道路安全和维护系统.
- 优化检测模型对于道路安全的实时应用至关重要.
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