对于铁路轨道的边缘引导的多尺度实例细分
Junting Lin1, Weikun Yang2, Xiaocheng Du2
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730030, China. linjt@lzjtu.edu.cn.
Scientific reports
|February 24, 2026
概括
新的铁路轨道细分模型SMDE-YOLO通过在复杂的环境中准确识别轨道来提高自主列车的安全性. 这种方法增强了边缘检测和特征融合,用于可靠的障碍物检测和导航.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自主列车运行需要精确的铁路轨道细分.
- 挑战包括模糊的界限和影响传统模型的复杂背景.
- 现有的方法在复杂的铁路环境中难以准确.
研究的目的:
- 引入SMDE-YOLO,这是一个用于铁路轨道细分的新型实例细分框架.
- 在复杂的铁路环境中解决当前模型的局限性.
- 为了提高自主列车系统的准确性和效率.
主要方法:
- 开发了基于YOLO11n-seg的SMDE-YOLO,结合了基于Scharr的边缘增强策略.
- 综合动态多分支特征金字塔网络 (DMBFPN) 用于多尺度特征建模.
- 利用多尺度边缘特征融合 (MSEFF) 和双增强高效脱分段头 (DEED-Seg) 来提高性能和减少计算.
主要成果:
- 在Railsem7750数据集上,SMDE-YOLO表现出卓越的性能.
- 该框架有效地处理模糊的轨道界限和复杂的背景.
- 在轻量化设计和高细分精度之间取得了平衡.
结论:
- 在具有挑战性的条件下,SMDE-YOLO为铁路轨道实例细分提供了有效的解决方案.
- 准确的轨道定位对于智能检查和安全区划至关重要.
- 拟议的模型提升了自主列车运行能力.
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