基于跨层次局部特征融合的光伏热斑缺陷检测

项新建; 孙思琪; 罗秋霞; 唐小辉; 陈祯力; 郑永平

doi:doi:10.19912/j.0254-0096.tynxb.2024-2032

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基于跨层次局部特征融合的光伏热斑缺陷检测

更新时间：2026-04-08

- 基于跨层次局部特征融合的光伏热斑缺陷检测
- 基于跨层次局部特征融合的光伏热斑缺陷检测
- 太阳能学报 2026年47卷第3期页码：732-738
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2032
  中图分类号： TP183
- 网络首发：2026-04-07，
  
  纸质出版：2026
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项新建, 孙思琪, 罗秋霞, 等. 基于跨层次局部特征融合的光伏热斑缺陷检测[J]. 太阳能学报, 2026,47(3):732-738.

项新建, 孙思琪, 罗秋霞, et al. 基于跨层次局部特征融合的光伏热斑缺陷检测[J]. 2026, 47(3): 732-738.
项新建, 孙思琪, 罗秋霞, 等. 基于跨层次局部特征融合的光伏热斑缺陷检测[J]. 太阳能学报, 2026,47(3):732-738. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2032.

项新建, 孙思琪, 罗秋霞, et al. 基于跨层次局部特征融合的光伏热斑缺陷检测[J]. 2026, 47(3): 732-738. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2032.

摘要

针对光伏热斑缺陷检测过程中

热斑的特征细节丢失而导致微小热斑漏检的问题

提出一种基于YOLOv8的跨层次局部特征融合改进方法。在YOLOv8中引入BiFormer思想改进主干网络

使模型能够更好地提取细节信息;使用Dysample动态上采样模块替代传统上采样方式

以更好地保留高分辨率特征图的细节信息;并设计跨层次局部特征融合的FCLAHead检测头

在不同层级的特征图间建立特征关联

实现信息互补

通过融合操作增强微小热斑的特征表达

提高模型在多场景下对微小热斑的检测能力。改进后的模型相较于基础模型YOLOv8n

平均精度mAP由86.3%提升至90%

精确率和召回率分别提升1.1个百分点和3.1个百分点

参数量仅为3.08×106

计算量略有增加

适用于精度要求高且硬件资源有限的应用场景。

Abstract

To address the issue of missed detections of small hotspots caused by the loss of detailed features in photovoltaic hotspot defect detection

this paper proposes an improved method for cross-level local feature fusion based on YOLOv8. The method introduces the BiFormer concept into the YOLOv8 backbone to enhance the model’s ability to extract detailed information. It replaces traditional upsampling methods with a Dysample dynamic upsampling module

better preserving the details of high-resolution feature maps. Additionally

an FCLAHead detection head is designed for cross-level local feature fusion

establishing feature associations across different feature map levels to achieve information complementarity. This fusion operation enhances the feature representation of small hotspots

improving the model’s detection capability for small hotspots across various scenarios. Compared to the base model YOLOv8n

the improved model increases mean average precision （mAP） from 86.3% to 90%

with precision and recall improving by 1.1 and 3.1 percentage points

respectively. The model’s parameter count is only 3.08×10

with a slight increase in computation

making it suitable for applications requiring high accuracy in hardware-limited environments.

关键词

Keywords

references

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