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.

本研究针对光伏电池制造过程中的缺陷检测问题，提出了一种轻量级的卷积神经网络PV-faultNet，以实现高效、实时的缺陷检测。此模型经过优化，参数仅为2.92百万，具备91%的精准度和89%的召回率，极大地降低了生产环境中的计算需求，具有良好的物理适用性和潜在的质量控制影响。

PV-faultNet：优化的卷积神经网络结构以检测有效光伏生产中的缺陷