Using SegFormer for Effective Semantic Cell Segmentation for Fault Detection in Photovoltaic Arrays

Abstract

Photovoltaic (PV) industries are susceptible to manufacturing defects within their solar cells. To accurately evaluate the efficacy of solar PV modules, the identification of manufacturing defects is imperative. Conventional industrial defect inspections predominantly rely on highly skilled inspectors conducting manual defect assessments, leading to sporadic and subjective identification outcomes. Deep-learning-based fault detection in PV or solar cells has emerged as a primary research area due to its superior efficiency and applicability. Hence, this study introduces a SegFormer-based fault detection framework to automate the visual defect inspection process in PV modules, complete with defect pseudocolorization. The proposed SegFormer-based framework effectively classifies defects into five categories: crack defects, front grid defects, interconnect defects, contact corrosion defects, and bright disconnect. Moreover, a comparative analysis is performed between the SegFormer model and the state-of-the-art fault detection algorithms, such as Deeplab v3, UNET, Deeplab v3+, PAN, PSPNet, and feature pyramid network (FPN). The experimental results reveal that the proposed SegFormer-based framework achieves highly encouraging performance, with a pixelwise accuracy of 96.24%, a weighted F1-score of 96.22%, an unweighted F1-score of 81.96%, and a mean intersection over union of 56.54%, outperforming other existing methods.