轻型SCL-YOLOv8:用于传输线路外来物体检测的高性能模型
Houling Ji1, Xishi Chen1, Jingpan Bai1
1School of Computer Science, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
本研究介绍了使用无人机在传输线路上实时检测异物的轻量级算法SCL-YOLOv8. 它显著降低了计算负载和模型大小,同时提高了检测准确性,提高了电网安全性.
科学领域:
- 电气工程
- 计算机视觉
- 人工智能
背景情况:
- 输电线路面临外来物体入侵的风险, 可能导致停电.
- 由于数据传输需求,目前的无人机检查缺乏实时分析和效率.
- 现有的YOLO模型对于有效部署无人机来说过于密集.
研究的目的:
- 在无人机平台上开发轻量级外来物体检测算法.
- 提高传输线路外来物体检测的效率和准确性.
- 减少无人机对象检测系统的计算复杂性和模型大小.
主要方法:
- 提出了基于YOLO的新型轻量级检测算法SCL-YOLOv8.
- 将CSPDarknet53的骨干替换为StarNet以减少计算复杂性和高效的功能提取.
- 引入了一个CGLU-ConvFormer模块,用于增强多尺度和本地特征提取.
- 改进了共享卷积层和组正常化检测头部,以减少计算和更好的特征融合.
主要成果:
- SCL-YOLOv8的平均精度 (mAP@0. 5) 为94. 2%.
- 与YOLOv8n相比,模型参数显著减少56.8%,浮点操作 (FLOP) 减少45.7%,模型大小减少50%.
- 证明了检测精度的提高和计算要求的降低.
结论:
- 通过无人机在传输线路上实时检测异物,SCL-YOLOv8提供了高效准确的解决方案.
- 轻量化设计可在资源有限的无人机平台上部署.
- 这一进步有助于提高电网安全性和运营效率.
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