基于MAP-YOLOv8的传输线绝缘器缺陷检测算法
1New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.
Scientific reports
|March 26, 2025
概括
本研究介绍了MAP-YOLOv8,这是一种用于检测输电线路绝缘体缺陷的先进算法. 改进的模型显著提高了实时电力线路检查的准确性和效率.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 稳定的电力供应日益增长的需求需要强大的输电线路基础设施.
- 绝缘体是传输线路中导体支和电绝缘的关键组件.
- 绝缘体状态的实时监控对于确保电力系统安全性和可靠性至关重要.
研究的目的:
- 开发一种高效准确的算法,用于检测输电线路绝缘体的缺陷.
- 改进现有的物体检测模型,以提高功率检查应用中的性能.
- 解决与绝缘体缺陷检测中的图像质量和细节损失相关的挑战.
主要方法:
- 提出了一种名为MAP-YOLOv8.8的新型传输线绝缘器缺陷检测算法.
- 通过集成GSConv和SimSPPF模块来增强YOLOv8网络,以优化计算和特征提取.
- 引入了一个新的注意力机制 (MAP-CA) 来融合全球和本地图像信息,提高识别精度.
- 实现超高分辨率重建以增强低分辨率绝缘体图像.
主要成果:
- MAP-YOLOv8模型的平均精度为96.6%,比基础YOLOv8模型提高了14%.
- 显示内存使用量为8.3 MB,F1得分为0.981.
- 实现了每秒89的检测速度,满足实时检测要求.
结论:
- 在传输线路绝缘器缺陷检测方面,MAP-YOLOv8提供了显著的进步.
- 该算法的高精度,效率和实时功能使其适合于实际的电力检查.
- 图像增强技术进一步提高了模型在识别微妙缺陷方面的有效性.
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