High Accuracy Detection Strategy for EL Defects in PV Modules Based on Machine Learning

A method of intelligent detection and precise localization of EL compositive defects in PV modules based on machine learning has been proposed. In the previous methods, the individual cell is taken as the inspection unit, however, this can not detect the holistic defects in PV modules. In this paper, two series-connected YOLOv5 networks with upstream and downstream process-dependent inspection model is established, which take a PV module as the detecting unit instead of a single cell. It can finish the detection and localization of 13 kinds of defects commonly found currently. These defects include not only those in a single cell, but also holistic defects that may appear after the cell assembling as a module. The experimental results show that both of detecting accuracy and speed are improved effectively for each PV module than before.