面向工业光伏系统的自进化缺陷检测框架
A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systems
AI总结：
本文提出SEPDD框架，通过自适应模型优化与持续自进化学习机制，解决工业光伏检测中分布偏移和新缺陷模式识别问题，实验表明其在公有和私有数据集上均表现优异。
摘要：
可靠的光伏电力生成需要及时检测可能降低能量产出、加速退化并增加生命周期运维成本的模块缺陷。电致发光（EL）成像已被广泛用于光伏模块检测。然而，由于异质模块几何结构、低分辨率成像条件、细微缺陷形态、长尾缺陷分布以及持续的数据偏移，自动化缺陷检测在真实运营环境中仍具挑战性。为解决这些问题，本文提出SEPDD，一种针对演进工业光伏检测场景的自进化光伏缺陷检测框架。SEPDD集成了自动化模型优化与持续自进化学习机制，使检测系统能够逐步适应分布偏移和新出现的缺陷模式。在公有光伏缺陷基准和私有工业EL数据集上的实验表明，所提框架的有效性。两个数据集均表现出严重的类别不平衡和显著的域偏移。SEPDD在公有数据集上达到91.4%的mAP50，在私有数据集上达到49.5%。在公有数据集上，其优于自主基线14.8%，优于人类专家4.7%；在私有数据集上，其优于自主基线4.9%，优于人类专家2.5%。
英文摘要：
Reliable photovoltaic (PV) power generation requires timely detection of module defects that may reduce energy yield, accelerate degradation, and increase lifecycle operation and maintenance costs during field operation. Electroluminescence (EL) imaging has therefore been widely adopted for PV module inspection. However, automated defect detection in real operational environments remains challenging due to heterogeneous module geometries, low-resolution imaging conditions, subtle defect morphology, long-tailed defect distributions, and continual data shifts introduced by evolving inspection and labeling processes. These factors significantly limit the robustness and long-term maintainability of conventional deep-learning inspection pipelines. To address these challenges, this paper proposes SEPDD, a Self-Evolving Photovoltaic Defect Detection framework designed for evolving industrial PV inspection scenarios. SEPDD integrates automated model optimization with a continual self-evolving learning mechanism, enabling the inspection system to progressively adapt to distribution shifts and newly emerging defect patterns during long-term deployment. Experiments conducted on both a public PV defect benchmark and a private industrial EL dataset demonstrate the effectiveness of the proposed framework. Both datasets exhibit severe class imbalance and significant domain shift. SEPDD achieves a leading mAP50 of 91.4% on the public dataset and 49.5% on the private dataset. It surpasses the autonomous baseline by 14.8% and human experts by 4.7% on the public dataset, and by 4.9% and 2.5%, respectively, on the private dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
