Challenge Introduction: Given the limitations of carbon-based resources for electricity production and the associated environmental problems, the use of renewable energy sources is on the rise globally, including in our country. Among renewable energy sources, solar power plants have been more widely adopted compared to other methods. Manufacturers claim that solar panels have a lifespan of 25 to 30 years. However, this is often not achieved due to the low quality of some solar panels. Some of these defects become apparent years after the solar power plant starts operating, when the contractors’ warranties have expired, leaving the plant owners with no recourse for compensation. Electroluminescence (EL) testing is the most accurate diagnostic test for solar panels. This test can identify defects that may reduce electricity production during the operational life of the solar power plant. In solar panel production lines, EL testing is performed on the panels at the final stage. The EL test image is stored along with the solar panel’s serial number. By processing EL images using artificial intelligence, defects can be identified, preventing the acceptance of defective panels. The goal of this challenge is to identify a common type of defect in EL test images of solar panels, known as Micro-Cracks. Sample images are provided below.   Evaluation Method: The team that correctly identifies the highest number of Micro-Cracks in the EL test images will win the challenge.   Detailed Description:

1. Importance of the Challenge:

– Environmental Impact: By ensuring the quality of solar panels, we can maximize their efficiency and lifespan, contributing to the reduction of carbon emissions and promoting sustainable energy sources. – Economic Benefits: Identifying defective panels before installation can save significant costs associated with maintenance and replacement, thus protecting investments in solar power plants. – Technological Advancement: Utilizing AI to enhance the precision of quality control processes in solar panel manufacturing can lead to more reliable and efficient production methods.  

2. Electroluminescence Testing:

– Process: EL testing involves applying a voltage to the solar panel and capturing the resulting light emission using specialized imaging equipment. This process highlights defects that are not visible to the naked eye. – Micro-Cracks: Micro-cracks are small fractures in the solar cells that can significantly impact the panel’s performance and durability. They are typically caused by mechanical stress during manufacturing, transportation, or installation.  

3. Data and Tools:

– EL Test Images: High-resolution grayscale images obtained from EL testing, where defects such as micro-cracks appear as dark lines or spots. – AI and Image Processing Techniques: Participants will use machine learning algorithms, particularly convolutional neural networks (CNNs), to analyze the images and detect micro-cracks. Tools such as Python, TensorFlow, Keras, and OpenCV may be used.  

4. Output Requirements:

– Defect Identification: The algorithm should output the coordinates or bounding boxes of the identified micro-cracks within the images. – Accuracy and Precision: The detection model should have a high true positive rate (correctly identifying actual micro-cracks) and a low false positive rate (incorrectly identifying normal areas as micro-cracks).  

5. Evaluation Criteria:

– Detection Accuracy: The percentage of correctly identified micro-cracks relative to the total number of micro-cracks present in the test images. – Processing Speed: The time required to analyze and process each image. – Scalability: The model’s ability to handle large datasets and its performance on unseen data. – Interpretability: The model should provide clear and understandable results, with the ability to visualize the identified defects.   By addressing this challenge, we aim to improve the quality control processes in solar panel manufacturing, ensuring that only the best panels are deployed in solar power plants, thereby enhancing their efficiency and longevity.