SSN: Shift Suppression Network for Endogenous Shift of Photovoltaic Defect Detection

Most of the existing photovoltaic (PV) defect detection methods are based on the assumption that the training and testing samples satisfy the independent identically distributed. However, in real PV scenarios, the endogenous shift problem exists widely, including background style shift and defect instance shift, which seriously affects the performance of detectors. In this article, we propose a novel network called shift suppression network (SSN) for the endogenous shift of PV defect detection, which consists of two core components, background style suppression (BSS) module and cross-layer graph reasoning (CGR) module. Specifically, BSS uses channel statistics matching alignment to adaptively suppress background style shifts without reliance on unknown domain data. CGR learns semantic dependencies between multiscale channel feature maps through cross-layer interaction, which improves the discriminative ability and localization ability in the face of defect instance shifts. To advance the study of endogenous shift, we provide the first electroluminescence (EL) endogenous shift dataset for PV modules, which creates by three groups of EL images collected at different times, totaling 16 323. The comprehensive evaluation results show that the SSN outperforms the state-of-the-art methods. Furthermore, we design an intelligent defect detection system for PV modules based on SSN. The system has higher detection accuracy and an intuitive visual interface, which can meet the actual detection function and requirements of the production line.