用轻量Transformer模型精准分割光伏热像图中的热点与蛇形缺陷。
Lightweight Transformer-Driven Segmentation of Hotspots and Snail Trails in Solar PV Thermal Imagery
- 基于SegFormer改进轻量架构，适配小而不规则缺陷检测。
- 在277张无人机热成像图上，Dice分数超基线模型12%以上。
- 模型轻量化支持边缘设备实时运行，适合大规模光伏巡检。
准确检测光伏组件中的热点和蛇形缺陷对保障能源效率和系统可靠性至关重要。本文提出一种监督式深度学习框架，用于分割光伏面板的热红外图像，数据集包含277张由DJI Matrice 100无人机搭载zenmuse XT红外相机拍摄的航拍热成像图。预处理流程包括图像缩放、基于CLAHE的对比度增强、去噪和归一化。开发了一种基于SegFormer的轻量级语义分割模型，采用定制Transformer编码器和简化解码器，并在人工标注缺陷区域的图像上进行微调。为评估性能，将本模型与U-Net、DeepLabV3、PSPNet和Mask2Former在一致预处理与增强条件下对比，评价指标包括每类的Dice分数、F1分数、Cohen's kappa、平均IoU和像素准确率。结果表明，该模型在精度与效率上均优于基线，尤其擅长小而形状不规则缺陷的分割。其轻量设计支持在边缘设备上实时部署，可无缝集成至无人机系统，实现大规模太阳能电站的自动化巡检。
原文摘要 · Abstract (English)
Accurate detection of defects such as hotspots and snail trails in photovoltaic modules is essential for maintaining energy efficiency and system reliablility. This work presents a supervised deep learning framework for segmenting thermal infrared images of PV panels, using a dataset of 277 aerial thermographic images captured by zenmuse XT infrared camera mounted on a DJI Matrice 100 drone. The preprocessing pipeline includes image resizing, CLAHE based contrast enhancement, denoising, and normalisation. A lightweight semantic segmentation model based on SegFormer is developed, featuring a customised Transformwer encoder and streamlined decoder, and fine-tuned on annotated images with manually labeled defect regions. To evaluate performance, we benchmark our model against U-Net, DeepLabV3, PSPNet, and Mask2Former using consistent preprocessing and augmentation. Evaluation metrices includes per-class Dice score, F1-score, Cohen's kappa, mean IoU, and pixel accuracy. The SegFormer-based model outperforms baselines in accuracy and efficiency, particularly for segmenting small and irregular defects. Its lightweight design real-time deployment on edge devices and seamless integration with drone-based systems for automated inspection of large-scale solar farms.
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