LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification
Solar-cell panels use sunlight as a source of energy to generate electricity. However, the performances of solar panels decline when they degrade, owing to defects. Some common defects in solar-cell panels include hot spots, cracking, and dust. Hence, it is important to efficiently detect defects in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/5/2112 |