MCP-YOLO:通过无人机进行实时绝缘体缺陷检查的精细边缘感知检测框架
Hongbin Sun1, Shijun Guo1, Xin Pan2
1School of Electrical and Information Engineering, Changchun Institute of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
新型深度学习模型MCP-YOLO通过提高缺陷检测准确性和效率来增强无人机对电力线绝缘体的检查. 这种轻量级的框架被优化为在自动化电网系统中实时边缘部署.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 无人机对输电线路绝缘体的检查对于电网维护至关重要.
- 现有的深度学习方法与复杂的背景,可变条件和边缘部署的计算效率作斗争.
- 准确和实时的缺陷识别对于防止停电至关重要.
研究的目的:
- 开发一种轻量级且准确的深度学习框架,用于使用无人机实时检测绝缘体缺陷.
- 在绝缘体检中应对复杂的背景和可变的成像条件的挑战.
- 创建一个适合于自动化电网系统中高效边缘部署的模型.
主要方法:
- 拟议的MCP-YOLO (多尺度复杂背景修剪YOLO) 框架与MS-EdgeNet用于增强边缘功能和DyFPN用于多尺度检测.
- 整合了一个辅助检测头,以提高训练监督和推断效率.
- 采用集团SLIM修剪,用于在不影响准确性的情况下进行模型压缩.
主要成果:
- 在3091张无人机图像的数据集上,MCP-YOLO实现了92.1%的mAP@0.5,90.5%的精度和89.0%的回忆.
- 该模型保持了小尺寸 (8.65M参数),并实现了250 FPS的快速推理速度.
- 与基线相比,模型尺寸减少了37.3%,检测性能优越,计算开销减少.
结论:
- MCP-YOLO为实时绝缘体缺陷检测提供了一个高度准确和计算高效的解决方案.
- 该框架的轻量级设计和增强的检测能力使其适合在自动化电网检查中实际部署.
- 拟议的创新有效地解决了无人机基础设施监控现有方法的局限性.
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