基于增强的ORB和YOLOv11的车下全景图像的轴型识别技术的研究
Xiaofan Feng1, Lu Peng1, Yu Tang2
1Research Institute of Highway Ministry of Transport, 8 Xitucheng Road, Beijing 100088, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一种新的全景成像系统,用于在收费站识别卡车轴类型. 该技术使用增强的ORB和YOLOv11进行准确,快速的车辆分类,改善道路管理.
科学领域:
- 计算机视觉 计算机视觉
- 运输工程 运输工程
- 机器学习 机器学习
背景情况:
- 国家政策规定了严格的卡车尺寸和重量限制,需要为收费管理准确的轴类型识别.
- 目前用于区分驱动和驱动轴的现有方法在准确性,设备复杂性和图像证据方面存在局限性.
- 有效的车辆分类对于收费站运营和遵守法规至关重要.
研究的目的:
- 开发用于车辆底盘的快速准确的全景图像检测技术.
- 为了克服轴型识别和图像保留当前方法的局限性.
- 提供一个便携且易于部署的系统,用于现场识别轴.
主要方法:
- 使用区域阵列摄像头开发了一个便携式车辆底盘图像采集系统,以便快速部署.
- 增强的ORB与FeatureBooster (FB) 模块和关键技术被用于高质量的全景图像拼接.
- YOLOv11算法通过ORB和FB功能进行了微调,用于轴类型识别.
主要成果:
- 增强的ORB算法与FB模块增加了特征匹配到151 ± 18.
- 综合的YOLOv11,ORB和FB方法实现了高性能指标:mAP@50为0.989和mAP@50:95为0.780.
- 该系统在夜间场景中表现出强的性能,mAP@50为0.977和mAP@50:95为0.743.
结论:
- 拟议的全景图像检测技术在车辆轴类型识别方面取得了重大进展.
- 该系统的可移植性,快速部署和高精度为收费站提供了有效的技术支持.
- 这项技术解决了符合运输政策的有效和可靠的轴识别需求.
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