Automated Quality Control and Defect Diagnosis for Photovoltaic Panels Using Annotated Image Data

This investigation exposes an automated technique for detecting photovoltaic (PV) panel defects from annotated images. The suggested work assigns each image a diagnostic label after analyzing the JSON annotations to identify defective modules. In order to gauge the frequency of defects and variations in the panels, the data are analyzed. The labeled dataset is examined through statistical methods. The impacts certify the pipeline’s ability to provide control and highlight notable differences. In addition, these missions lay the foundation for creating automated detection.