基于改进YOLOv8的红外光伏组件缺陷检测方法

孙廨尧; 孙俊海; 朱先远; 李春秋

doi:doi:10.19912/j.0254-0096.tynxb.2024-1871

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基于改进YOLOv8的红外光伏组件缺陷检测方法

更新时间：2026-03-06

- 基于改进YOLOv8的红外光伏组件缺陷检测方法
- Vol. 47, Issue 2, Pages: 148-154(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1871
  CLC： TM615
- Online First：05 March 2026，
  
  Published：05 March 2026
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孙廨尧, 孙俊海, 朱先远, et al. 基于改进YOLOv8的红外光伏组件缺陷检测方法[J]. 2026, 47(2): 148-154.
DOI：

孙廨尧, 孙俊海, 朱先远, et al. 基于改进YOLOv8的红外光伏组件缺陷检测方法[J]. 2026, 47(2): 148-154. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1871.

摘要

针对传统红外光伏组件缺陷检测精确低、速度慢问题

提出一种基于改进YOLOv8的红外光伏组件缺陷检测方法WD_YOLOv8。首先引入Wise-IoU v2损失函数替换原算法中的CIoU损失

提升检测精确率

同时提高精确率P和召回率R之间的平衡;其次

在特征融合网络Neck中采用DySample上采样

保持较好的检测帧率。实验表明

改进后模型在红外伪彩色光伏组件缺陷检测场景下指标P、mAP@0.5和F1-score分别提高0.3、1.5和2.6个百分点;在红外灰度光伏组件缺陷检测场景下指标P、mAP@0.5和F1-score分别提高18.6、7.7和5.1个百分点。改进后算法在红外场景下具有良好的鲁棒性和适应性。

Abstract

Traditional infrared photovoltaic module defect detection methods often suffer from low accuracy and slow processing speeds. To address these limitations

we propose WD_YOLOv8

an improved infrared photovoltaic module defect detection method based on YOLOv8. First

we introduced the Wise Intersection over Union v2 loss function to replace the Complete-IoU loss function used in the original algorithm

thereby enhancing detection precision. Additionally

the v2 loss function improved the balance between precision （） and recall （）

optimizing overall detection performance. Second

DySample up-sampling was incorporated into the feature fusion network （Neck） to maintain a high detection frame rate. Experimental results show that the improved model increases metrics

mAP@0.5

and F1-score by 0.3

1.5

and 2.6 percentage points

respectively

in the infrared pseudo-color photovoltaic module defect detection scenarios. Moreover

in infrared grayscale photovoltaic module defect detection scenarios

these metrics improve by 18.6

7.7

and 5.1 percentage points

respectively. These findings indicate that the proposed algorithm exhibits strong robustness and adaptability in infrared imaging applications.

关键词

Keywords

references

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