一种用于铁路运输场景的智能障碍探测方法
Zhao Sheng1, Tianyang Liu2, Wei Shangguan3
1Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究介绍了ACX-YOLOv8,这是一种用于铁路轨道的智能障碍物检测系统. 增强的YOLOv8模型准确地实时识别异物,提高火车运行安全.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 铁路工程 铁路工程是指铁路工程.
背景情况:
- 传统的铁路监控系统缺乏实时外来物体检测能力.
- 确保火车运行安全需要先进的障碍物识别方法.
研究的目的:
- 开发一种智能视觉传感方法,用于在铁路轨道区域检测异物.
- 通过使用改进的YOLOv8模型,提高障碍物检测的准确性和效率.
主要方法:
- 该ACX-YOLOv8模型集成了空间坐标注意力机制 (SCAM) 和级联扩展卷积 (CDConv).
- 使用X6脱的检测头来提高检测准确度和推断速度.
- 该模型是建立在对象检测的YOLOv8框架上.
主要成果:
- 该ACX-YOLOv8模型的平均精度 (mAP50) 为87.1%,比基线YOLOv8.7改进了2.7%.
- 该模型在PASCAL VOC数据集上显示了1.8%的mAP50增加,验证了其概括能力.
- 优化的模型有485万个参数,平衡轻量特性与高检测精度.
结论:
- ACX-YOLOv8可快速准确地检测铁路轨道上的外来物体.
- 该算法为复杂的铁路环境中的实时障碍物监测提供了可靠的技术支持.
- 这有助于提高铁路运营的安全性和智能化.
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