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铁路入侵风险量化与轨道语义细分和时空特征
Shanping Ning1,2, Feng Ding1, Bangbang Chen1
1School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710016, China.
本研究引入了一种使用人工智能驱动的轨道细分和时空分析来量化铁路入侵风险的新方法. 它通过为潜在威胁提供数据驱动的,分级的早期警告来提高火车的安全性.
科学领域:
- 人工智能的人工智能
- 铁路工程 铁路工程是指铁路工程.
- 计算机视觉 计算机视觉
背景情况:
- 在铁路区域外来物体的入侵带来了重大安全风险.
- 目前的视觉检测方法缺乏定量风险评估能力.
研究的目的:
- 开发一种铁路入侵风险量化方法,整合轨道语义细分和时空特征.
- 通过定量风险评估和分级预警来提高火车运行安全.
主要方法:
- 使用了改进的BiSeNetV2网络来准确地提取轨道区域.
- 根据铁路结构宽度标准构建的物理约束风险区.
- 开发了一种轻量级的检测架构,配备了扩展变压器模块,以提高精度,特别是对于小物体.
- 综合物体类别重量,横向风险系数,纵向距离衰减和速度补偿,用于全面的风险评估.
主要成果:
- 在专有数据集上实现了84.9%的平均平均精度 (mAP).
- 在入侵检测准确度方面,基线模型的表现优于3.3%.
- 证明了对定量入侵风险评估和分级预警的能力.
结论:
- 拟议的方法可以为主动列车保护系统提供数据驱动的决策支持.
- 通过将横向距离检测与多维风险指标相结合,显著提高智能铁路安全保护能力.
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