An in-depth analysis and summary of machine learning techniques for solar cell defect detection using electroluminescence images. This project explores the effectiveness of various ML and DL models in classifying solar panel defects, addressing challenges of imbalanced data, and providing practical insights for industrial applications.
Key features:
- Comprehensive summaries of research methodologies and findings
- Comparative analysis of ML vs DL models for image classification
- Insights on handling imbalanced datasets in solar technology
- Practical guidelines for model selection in solar cell quality control
- Reflections on the process of analyzing and summarizing technical research
This repository documents the journey of extracting key insights from complex scientific literature and translating them into actionable knowledge for real-world applications in renewable energy technology.