优化了YOLOv11m,用于实时高速铁路连锁系统缺陷检测
Tao Jin1, Zhijun Shen2,3, Haowen Geng1
1School of Computer and Information Engineering, Fuyang Normal University, Fuyang, 236037, Anhui, People's Republic of China.
Scientific reports
|December 13, 2025
概括
本研究介绍了MSIM-YOLOv11m,这是一个针对实时铁路连锁故障检测的优化模型,显著改善了小物体识别和减少计算负载,以实现高效的检查.
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:
- 在高速铁路连锁系统中实时检测缺陷对于安全和维护至关重要.
- 现有的物体检测模型,如YOLO,面临着小组件和高计算需求的挑战.
- 局限性包括难以检测小部件 (如钉) 和平台限制.
研究的目的:
- 开发一个优化的对象检测模型,用于实时检查铁路连锁组件.
- 解决现有模型在检测小物体和管理计算成本方面的局限性.
- 为自动缺陷检测提供轻量级和准确的解决方案.
主要方法:
- 开发了一个优化的YOLOv11m模型,称为MSIM-YOLOv11m.
- 整合了三个新型模块:大型可分离内核注意力 (LSKA),双向特征金字塔网络 (BiFPN) 和自适应内核卷积 (AKConv).
- 在专用连锁数据集上评估的模型用于缺陷检测性能.
主要成果:
- MSIM-YOLOv11m模型实现了78.3%的mAP50-95和64.7%的小目标AP.
- 与YOLOv9m模型相比,计算成本降低了50.5%.
- 该模型在检测连锁电缆组件上的小型缺陷方面被证明是有效的.
结论:
- MSIM-YOLOv11m为实时铁路连锁检查提供了一个轻量级和准确的解决方案.
- 拟议的模型有效地克服了与小物体检测和计算效率相关的挑战.
- 这一进步支持通过自动化视觉检查增强安全性和维护.
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