基于改进YOLOv8的光伏组件缺陷检测研究

崔建伟; 王月明

doi:doi:10.19912/j.0254-0096.tynxb.2024-0913

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基于改进YOLOv8的光伏组件缺陷检测研究

更新时间：2025-12-17

- 基于改进YOLOv8的光伏组件缺陷检测研究
- Issue 10, Pages: 189-196(2025)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0913
  CLC： M914
- Published Online：31 October 2025，
  
  Published：31 October 2025
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崔建伟, 王月明. 基于改进YOLOv8的光伏组件缺陷检测研究[J]. 2025, (10): 189-196.
DOI：

崔建伟, 王月明. 基于改进YOLOv8的光伏组件缺陷检测研究[J]. 2025, (10): 189-196. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0913.

摘要

针对航拍高分辨率图像下光伏组件的鸟粪与遮挡缺陷以及二极管损坏形成光斑缺陷的检测问题

提出改进深度学习模型YOLOv8n结合切片辅助超推理的检测算法。以2667张可见光与热成像图像进行切图后标注鸟粪、遮挡、光斑3种缺陷构建光伏组件数据集。首先

在YOLOv8n的主干网络上

通过添加3层CBS模块

构建小目标检测层

加强小目标特征信息的传递。其次

在Backbone部分添加全局注意力机制

采用通道与空间注意力的框架

使模型能更好地捕捉全局特征信息。基于上述两处对网络结构的改进设计其消融实验

实验结果表明改进模型相较于基础模型的mAP50值和mAP50∶95分别提升4.3%和1.5%;设计改进模型与其他目标检测模型的对比实验

实验结果表明改进模型的准确率优于其他检测模型。最后

结合切片辅助超推理进行先切片后检测

设计添加切片辅助超推理的整体模型对比实验

实验结果表明改进后的YOLOv8-PG+SAHI模型对高分辨率光伏组件图像下小目标缺陷的检测能力最优

准确率可达88.73%。上述实验表明改进模型更适合航拍高分辨率图像下光伏组件的小目标检测。

Abstract

Aiming at the problem of detecting bird droppings and shading defects as well as light spot defects formed by diode damage in photovoltaic modules under aerial high-resolution images

a detection algorithm combining the improves deep learning model YOLOv8n with slice-assisted super-reasoning is proposed. The PV module dataset is constructed by slicing 2667 visible and thermal imaging images and labelling them with three kinds of defects： bird droppings

shading and light spots. Firstly

on the backbone network of YOLOv8n

a small target detection layer is constructed by adding a 3-layer CBS module to enhance the transmission of small target feature information. Secondly

a global attention mechanism is added to the Backbone part

adopting the framework of channel and spatial attention

so that the model can better capture the global feature information. Based on the above two improvements to the network structure to design its ablation experiments

the experimental results show that the improved model improves the mAP50 value and mAP50：95 by 4.3% and 1.5%

respectively

compared with the base model; to design the comparison experiments between the improved model and the other target detection models

and the experimental results show that the improved model's accuracy is better than that of the other detection models. Finally

combined with slice-assisted hyper-reasoning for slicing before detection

the overall model comparison experiments are designed with the addition of slice-assisted hyper-reasoning

and the experimental results show that the improved YOLOv8-PG+SAHI model is optimal for detecting small target defects in high-resolution photovoltaic module images

and the accuracy rate can reach 88.73%. The above experiments show that the improved model is more suitable for small target detection of PV modules under aerial high-resolution images.

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