基于改进YOLOv5模型的光伏组件积灰量检测

李乐; 刘逸飞; 李浩义; 吴欣玥; 尹晓萱; 吴皓月

doi:10.19912/j.0254-0096.tynxb.2025-0807

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基于改进YOLOv5模型的光伏组件积灰量检测

更新时间：2026-10-15

- 基于改进YOLOv5模型的光伏组件积灰量检测
- Vol. 47, Issue 9, Pages: 352-361(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0807
  CLC： TM615
- Online First：10 October 2026，
  
  Published：2026
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李乐, 刘逸飞, 李浩义, et al. 基于改进YOLOv5模型的光伏组件积灰量检测[J]. 2026, 47(9): 352-361.
DOI：

李乐, 刘逸飞, 李浩义, et al. 基于改进YOLOv5模型的光伏组件积灰量检测[J]. 2026, 47(9): 352-361. DOI： 10.19912/j.0254-0096.tynxb.2025-0807.

摘要

为解决光伏组件表面积灰引起的发电效率下降、运行寿命降低的问题

该文提出一种用于积灰检测的改进YOLOv5模型

通过引入基于多尺度的GS卷积模块与VoVGSCSP模块

添加CBAM注意力机制并改进SPPF模块

实现检测精度与速度双重提升。在获取充足数据集基础上

对部分数据集采用限制对比度的自适应直方图均衡化（CLAHE）、锐化与Gamma参数调节协同处理

并采用PyCharm搭建基于改进YOLOv5模型的实验平台

实现光伏组件表面积灰位置和区域积灰量的高效率检测验证。结果表明

改进YOLOv5模型能使区域积灰量检测的mAP@0.5∶0.95较传统YOLOv5模型提升3.3个百分点

检测速度由65.2帧/s提升至68.32帧/s

具备精度和速度双重优势。该研究建立了一种适用于不同光照条件下的光伏组件表面积灰检测模型

可为光伏电站运维管理与清洁决策提供参考。

Abstract

To address the decline in power generation efficiency and the shortened operational lifespan of photovoltaic （PV） modules caused by surface dust accumulation

this paper proposes an improved YOLOv5-based model for dust detection. The proposed model enhances the original YOLOv5 architecture by introducing multi-scale GS convolution modules and VoVGSCSP structures

incorporating the CBAM attention mechanism

and modifying the SPPF module to improve both accuracy and speed. Based on a sufficiently large and representative dataset

CLAHE

sharpening

and Gamma parameter adjustment are collaboratively applied to part of the dataset. An experimental platform was developed in PyCharm to implement and verify the improved model

enabling efficient detection of dust locations and regional dust accumulation on PV module surfaces. Experimental results show that the improved YOLOv5 model achieves 3.3 percentage points increase in @0.5∶0.95 over the baseline YOLOv5

while the FPS improves from 65.2 FPS to 68.3 FPS

demonstrating advantages in both accuracy and speed. This study develops a surface dust detection model for photovoltaic modules under varying illumination conditions

providing methodological support for operation and maintenance management and cleaning decision-making in photovoltaic power plants.

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