基于HCF-YOLO的光伏组件红外图像热斑故障检测

秦怡鸣; 尹丽菊; 高晓宁; 王峰

doi:doi:10.19912/j.0254-0096.tynxb.2024-1096

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基于HCF-YOLO的光伏组件红外图像热斑故障检测

更新时间：2025-12-17

- 基于HCF-YOLO的光伏组件红外图像热斑故障检测
- Issue 10, Pages: 285-294(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1096
  CLC： M93
- Published Online：31 October 2025，
  
  Published：31 October 2025
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秦怡鸣, 尹丽菊, 高晓宁, et al. 基于HCF-YOLO的光伏组件红外图像热斑故障检测[J]. 2025, (10): 285-294.
DOI：

秦怡鸣, 尹丽菊, 高晓宁, et al. 基于HCF-YOLO的光伏组件红外图像热斑故障检测[J]. 2025, (10): 285-294. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1096.

摘要

通过无人机拍摄光伏组件红外图像进行检测时

图像背景较为复杂

且其中含有的小目标热斑故障在检测过程中容易丢失信息

出现误检或漏检等状况。针对上述问题

将HCF-Net与YOLOv8n网络结合

提出一种融合网络（HCF-YOLO）用于光伏组件红外图像热斑故障检测。加入并行化注意力机制（PPA）

通过分层特征融合和注意力机制来增强小目标的表达

确保在多个降采样步骤后保留热斑关键信息。采用维度感知选择性集成模块（DASI）

注重对高维和低维特征的自适应选择和精细融合

增强小目标的显著性。使用PIoU作为HCF-YOLO的损失函数

在回归的早期阶段

引导预测框沿有效路径回归

提升检测速度。HCF-YOLO算法相较于原有的YOLOv8n算法

检测精度（AP50）从89.27%提升至97.28%

并且检测速度达到217.33帧/s

实验结果可证明模型的有效性。

Abstract

When using drones to capture infrared images of photovoltaic modules for detection

the images often exhibit complex backgrounds

leading to challenges in detecting small target hot spots

which may result in information loss

false positives

or false negatives. In response to these issues

a novel fusion network（HCF-YOLO） is proposed by integrating the HCF-Net with the YOLOv8n network for detecting hot spot faults in infrared images of photovoltaic modules. This fusion network incorporates a Parallelized Patch-Aware Attention Module（PPA） to enhance the representation of small targets through hierarchical feature fusion and attention mechanisms

ensuring the retention of crucial hot spot information after multiple downsampling steps. Additionally

a Dimension-Aware Selective Integration Module（DASI） is employed to adaptively select and finely integrate high-dimensional and low-dimensional features

thereby enhancing the saliency of small targets. The Powerful IoU（PIOU） is adopted as the loss function for HCF-YOLO

guiding the early-stage regression of predicted boxes along effective paths to improve detection speed. The HCF-YOLO algorithm demonstrates a significant enhancement in detection accuracy

with the average precision at an intersection over union of 50%（AP50） increasing from 89.27% to 97.28% compared to the original YOLOv8n algorithm

achieving a detection speed of 217.33 frame/s. According to the experimental results

the effectiveness of the model is validated.

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