A Novel Algorithm for Thermal Imaging Fault Detection in Photovoltaic Panels

Due to the complex background and low resolution of photovoltaic (PV) panel image, the fault detection accuracy of photovoltaic panel is low. Therefore, we devise an efficient asymptotic feature pyramid network (EAFPN) to enhance the detection accuracy. Initially, an efficient spatial and channel attention mechanism (ESCAM) module is proposed, which mitigates the impact of complex backgrounds in the feature extraction network. Meanwhile, we introduce an asymptotic feature pyramid network (AFPN) module into the feature fusion network, which effectively extracts semantic information, captures the low-level localization features, and prevents the lose and degradation of small object information. In addition, we integrate content-aware reassembly of features (CARAFE) module to the feature fusion network, which can further enhance the accuracy. Furthermore, we generate the Chang'an University Photovoltaic Panel Thermal Imaging Defect (CHD-PVTID) datasets, which containing six common faults, and conducted experiments. The results show that EAFPN algorithm has higher accuracy in identifying fault types than other algorithms.