增强的YOLOv11框架用于无人机光伏检查中的精确多故障检测.
Shufeng Meng1, Yang Yue2, Tianxu Xu1
1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了改进的YOLOv11框架,用于检测光伏 (PV) 异常,如污点,缺陷和雪. 改进的模型同时准确地分类所有三种故障类型,提高能源产量,并使智能光伏检查成为可能.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 材料科学 材料科学 材料科学
背景情况:
- 光伏 (PV) 能量的产量因常见的异常,如污,缺陷和积雪等而显著减少.
- 现有的检测方法往往会损害速度的准确性,并且无法同时识别所有三个主要的光伏故障类型.
- 对于全面的光伏异常评估,需要先进,准确和同时检测系统.
研究的目的:
- 开发一种增强的物体检测框架,能够准确且同时分类三种普遍的光伏异常:污点,缺陷和雪.
- 提高光伏 (PV) 故障检测的准确性和效率,以提高能源产量和系统可靠性.
- 引入新的深度学习组件,用于在光伏异常识别中提取高级特征和理解上下文.
主要方法:
- 开发了一个增强的YOLOv11框架,结合色调和值 (HSV) 颜色模型来进行强大的颜色特征提取.
- 一个前景关注模块被集成到骨干中,用于精确的微缺陷边界划分.
- 在检测头中使用混合结构块,通过编码全球上下文和细粒度细节来改进对小物体的识别.
- 用边界的西格线性单元 (B-SiLU) 激活功能来优化梯度流和特征歧视.
主要成果:
- 增强的YOLOv11框架显示了整体平均平均精度 (mAP) 的显著改善,增加了1.8%.
- 在检测缺陷 (2.2%),污点 (3.3%) 和雪 (0.8%) 方面观察到特定的精度增长.
- 梯度加权类激活映射 (Grad-CAM) 可视化证实了该模型对相关断层区域的专注关注.
结论:
- 提议的增强YOLOv11框架为智能光伏检查和早期故障检测提供了可靠和准确的解决方案.
- 集成先进的深度学习技术,包括HSV颜色建模和注意力机制,显著提高了PV异常分类的性能.
- 这项研究有助于推进自动化光伏系统监控,从而提高运营效率和能源生产.
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