轻量CNN模型PV-faultNet实现光伏电池缺陷实时检测
PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production
- 设计轻量化CNN架构，仅292万参数，适配资源受限设备
- 在数据稀缺下仍达91%精度、89%召回率，F1-score为90%
- 适合工业级光伏产线部署，可提升质检效率与准确性
全球向可再生能源转型推动了光伏电池制造的重要性，因其是绿色能源的基础单元。然而，制造过程复杂，易产生缺陷，影响整体效率。当前主要依赖人工检测，存在偏见、耗时且成本高；尽管已有自动化方案，但多数资源消耗大，难以在生产环境中应用。为此，本文提出PV-faultNet，一种专为光伏电池缺陷实时检测优化的轻量级卷积神经网络（CNN），可部署于资源受限的生产设备。该模型仅含292万参数，显著降低计算负担，同时保持高准确率。通过综合数据增强技术应对数据稀缺问题，有效提升泛化能力，在精度、召回率与F1分数上分别达到91%、89%和90%，展现出在光伏生产中规模化质量控制的强大潜力。
原文摘要 · Abstract (English)
The global shift towards renewable energy has pushed PV cell manufacturing as a pivotal point as they are the fundamental building block of green energy. However, the manufacturing process is complex enough to lose its purpose due to probable defects experienced during the time impacting the overall efficiency. However, at the moment, manual inspection is being conducted to detect the defects that can cause bias, leading to time and cost inefficiency. Even if automated solutions have also been proposed, most of them are resource-intensive, proving ineffective in production environments. In that context, this study presents PV-faultNet, a lightweight Convolutional Neural Network (CNN) architecture optimized for efficient and real-time defect detection in photovoltaic (PV) cells, designed to be deployable on resource-limited production devices. Addressing computational challenges in industrial PV manufacturing environments, the model includes only 2.92 million parameters, significantly reducing processing demands without sacrificing accuracy. Comprehensive data augmentation techniques were implemented to tackle data scarcity, thus enhancing model generalization and maintaining a balance between precision and recall. The proposed model achieved high performance with 91\% precision, 89\% recall, and a 90\% F1 score, demonstrating its effectiveness for scalable quality control in PV production.
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