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一个轻量级的轨道特征检测算法,基于元素乘法和扩展路径聚合网络
Hong Qiu1, Dayong Yang1, Juanhua Cao2
1School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了YOLO-LWTD,这是一款用于轨道检查的轻量级模型,显著提高了实时性能并减少了模型大小. 增强的YOLO-LWTD为嵌入式系统中的轨道特征检测提供了更有效的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 现有的轨道检查方法面临着高计算负载和实时性能不足的挑战.
- 大型模型参数阻碍了在资源有限的环境中高效部署.
研究的目的:
- 开发一个轻量级的轨道特征检测模块 (YOLO-LWTD),以解决计算和实时性能问题.
- 提高嵌入式系统轨道检查的效率和准确性.
主要方法:
- 将StarNet模块集成到骨干网络中,以改善特征表征.
- 使用轻量级的延长路径聚合网络,在子中使用C3K2-Light,以实现高效的多尺度特征融合.
- 使用更轻的检测头 (Detect-LADH) 来减少功能解码的复杂性.
主要成果:
- 与基准相比,YOLO-LWTD模型的精度提高了0.5%,回忆率提高了2.0%,平均平均精度 (MAP) 提高了0.8%.
- 推理速度提高了38.1%,达到163 FPS.
- 模型尺寸被压缩到1.5 MB,轻量化率为71.1%.
结论:
- 拟议的YOLO-LWTD模型为轨道检测提供了一种节能且轻量化的解决方案.
- 该模型在精度,回忆和速度方面表现出卓越的性能,使其适合嵌入式部署和实时处理.
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