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.

翻译：全球向可再生能源的转型使光伏电池制造成为关键环节，因其是绿色能源的基础构成单元。然而，制造过程本身足够复杂，可能在生产期间出现的缺陷会影响整体效率，从而使其失去应有价值。目前，缺陷检测主要依赖人工检查，这种方式易产生偏差，并导致时间和成本效益低下。尽管已有自动化解决方案被提出，但其中多数计算资源消耗大，在生产环境中效果有限。在此背景下，本研究提出PV-faultNet——一种轻量化的卷积神经网络（CNN）架构，专为光伏（PV）电池的高效实时缺陷检测而优化，旨在可部署于资源受限的生产设备上。针对工业光伏制造环境中的计算挑战，该模型仅包含292万个参数，在保持精度的同时显著降低了处理需求。研究采用了全面的数据增强技术以应对数据稀缺问题，从而提升了模型的泛化能力，并保持了精确率与召回率之间的平衡。所提模型取得了91%的精确率、89%的召回率以及90%的F1分数，展现出其在光伏生产中可扩展质量控制方面的有效性。