基于改进YOLOv8的光伏缺陷快速检测

赵永辉; 李振; 金帅; 颜培钰; 李超; 刘淑玉

doi:doi:10.19912/j.0254-0096.tynxb.2024-1880

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基于改进YOLOv8的光伏缺陷快速检测

更新时间：2026-04-08

- 基于改进YOLOv8的光伏缺陷快速检测
- 基于改进YOLOv8的光伏缺陷快速检测
- 太阳能学报 2026年47卷第3期页码：584-593
- 作者机构：
- 作者简介：
- 基金信息：
  
  光伏发电系统数字化评价技术研究（2022ZXJ04A03）;黑龙江省自然科学基金项目（LH2023F003）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1880
  中图分类号： TP391
- 网络首发：2026-04-07，
  
  纸质出版：2026
- 稿件说明：
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赵永辉, 李振, 金帅, 等. 基于改进YOLOv8的光伏缺陷快速检测[J]. 太阳能学报, 2026,47(3):584-593.

赵永辉, 李振, 金帅, et al. 基于改进YOLOv8的光伏缺陷快速检测[J]. 2026, 47(3): 584-593.
赵永辉, 李振, 金帅, 等. 基于改进YOLOv8的光伏缺陷快速检测[J]. 太阳能学报, 2026,47(3):584-593. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1880.

赵永辉, 李振, 金帅, et al. 基于改进YOLOv8的光伏缺陷快速检测[J]. 2026, 47(3): 584-593. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1880.

摘要

针对现有光伏组件电致发光（EL）的缺陷检测中存在的背景干扰和计算冗余问题

以及模型精度与速度难以平衡的挑战

提出一种改进的YOLOv8光伏EL缺陷检测方法：YOLOv8-LSB。首先

在主干网络中引入SCConv卷积模块

以降低空间冗余并增强小目标特征提取能力;其次

在颈部添加LSK注意力机制

降低背景干扰;同时采用BiFPN结构提升多尺度特征融合能力

更好地捕捉不同方面的特征。最后

将Inner-CIoU作为边界框回归损失函数

提高回归精度和收敛速度。实验结果显示

YOLOv8-LSB在mAP@0.5上达91.2 %

FPS达170.2 帧/s

相较于基准模型YOLOv8n

平均精度提高2.6个百分点

FPS提升4.8 帧/s

实现了更实时且准确的光伏EL缺陷检测。

Abstract

This paper addresses the challenges of background interference

computational redundancy

and the difficulty in balancing model accuracy with processing speed in existing photovoltaic module electroluminescence （EL） defect detection methods. We propose an enhanced YOLOv8-based defect detection method

named YOLOv8-LSB

to tackle these issues. Firstly

we introduce the SCConv convolution module into the backbone network

reducing spatial redundancy while improving the extraction of small target features. Secondly

we incorporate the LSK attention mechanism in the neck to effectively mitigate background interference. Additionally

we use the BiFPN structure to enhance multi-scale feature fusion

enabling the model to capture features from various perspectives more effectively. Lastly

we employ Inner-CIoU as the bounding box regression loss function

which improves both regression accuracy and convergence speed. Experimental results show that YOLOv8-LSB achieves 91.2% mAP@0.5 and 170.2 FPS. Compared to the baseline model YOLOv8n

the proposed method improves average accuracy by 2.6 percentage points and FPS by 4.8 frames per second

making it a more real-time and accurate solution for photovoltaic EL defect detection.

关键词

Keywords

references

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