Study on performance evaluation of intelligent photovoltaic defect detection by electroluminescence imaging

Machine vision is not a mere upgrade of the specification of the current imaging devices, but rather a form of visual perception technology that involves intelligent modules in the processes of measurement, processing, and decision- making. Given the novel functionalities and features of machine vision-based intelligent detection devices, the traditional evaluation methods based on testing the physical parameters of imaging devices need further refinement and development. Taking the electroluminescence (EL) imaging in photovoltaic (PV) tests as an example, we investigate the influence of changes in dataset characteristics on the performance of object detection by combining digital image processing and deep learning methods. Features regarding to the crack-type defect datasets, such as the grayscale, contrast, shape and resolution, are controlled and adjusted based on new generated datasets from the original datasets. From the numerical experiments, some new aspects for evaluating the intelligent detection.