Identifying solar panel defects with a CNN

Engineering

• Solar Panel 100%
• Dataset 75%
• Defects 50%
• Classification 50%
• Development 25%
• Energy Engineering 25%
• Efficiency 25%
• Production 25%
• Internals 25%
• Accuracy 25%
• Hazards 25%
• Input Data 25%
• Complexity 25%
• Pipeline 25%
• Detection Process 25%

Computer Science

• Features Detection 100%
• Image Processing 50%
• Classification 50%
• Preprocessing 50%
• Convolutional Neural Network 50%
• Complexity 25%
• Identification 25%
• Accuracy 25%
• Neural Network 25%
• Straightforward Approach 25%
• Detection Process 25%
• Final Identification 25%