电气化铁路连锁系统的异物检测数据集和网络.
Fengqi Li1, Jinhao Cao1, Haolin Yang1
1School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian, 116028, China.
Scientific reports
|February 14, 2026
概括
在铁路连锁系统中准确检测异物对安全至关重要. 这项研究引入了一种新的检测网络,可以显著提高这些物体的识别,即使是小物体,在复杂的环境中.
科学领域:
- 计算机视觉 计算机视觉
- 铁路工程 铁路工程是指铁路工程.
- 人工智能的人工智能
背景情况:
- 在电气化铁路连锁系统中外来物体的入侵带来了重大安全风险,包括电源故障和运营中断.
- 现有的外来物体检测方法在复杂的铁路环境,各种物体形状和不同物体尺寸中面临挑战.
- 专用铁路数据集的稀缺性阻碍了可靠的检测模型的开发和评估.
研究的目的:
- 开发和验证一种有效的深度学习模型,用于在电气化铁路连锁系统中检测异物.
- 解决当前处理复杂环境和各种物体特征的方法的局限性.
- 为培训和评估铁路外来物体检测模型提供新的数据集.
主要方法:
- 构建一个新的铁路连锁外国对象数据集.
- 铁路连锁外来物体检测网络的建议,使用Swin变压器进行多尺度特征提取和全球关系建模.
- 实施一个多分支的融合特征金字塔网络,以提高跨规模的功能融合.
- 整合一个区域感应场增强边缘模块,以改进对延长物体的检测.
主要成果:
- 拟议的网络在构建的数据集上实现了60.2%的平均精度 (AP).
- 小物体检测性能显著提高,达到53.8%的AP.
- 该模型在复杂的铁路场景中有效区分前景对象和背景.
结论:
- 开发的铁路连锁外来物体检测网络有效地解决了在复杂的铁路环境中检测外来物体的挑战.
- 提出的方法,包括Swin变压器和特征聚变技术,显著提高检测准确度,特别是对于小型和长长的物体.
- 铁路连锁外来物体数据集的创建为铁路安全这一关键领域的未来研究提供了宝贵的资源.
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