Experimental Validation of AI-Based Defect Detection in Solar Panels

The document describes an internship opportunity focused on developing and validating AI-based defect detection methods for solar panels using advanced imaging techniques.

• Solar panel defect detection focus: The project aims to improve reliability and efficiency in solar panels by detecting defects like micro-cracks and broken cells through AI combined with imaging methods such as Photoluminescence (PL), Electroluminescence (EL), and RGB photography.

• Experimental workflow and tasks: The internship involves building a lab setup with a 3D gantry and imaging systems, capturing and comparing various images, annotating data, training AI models for defect classification, and evaluating imaging and AI performance.

• Learning and skills development: Interns will gain experience in advanced imaging techniques, hardware setup and calibration, data annotation for AI, machine learning model training and validation, and analysis of imaging modalities for industrial use.