Research Article
Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis
Table 13
Performance comparison of proposed method with various state-of-the-art techniques.
| Reference | Methodology used | Classification accuracy (%) |
| [38] | DeepSolarEye (ResNet-based) | 97.80 | [39] | Yolo V3 | 96.30 | [13] | Custom CNN | 79.06 | [40] | Custom CNN | 94.30 | [16] | Custom CNN | 97.90 | [41] | Custom CNN | 95.07 | [42] | Pretrained CNN+random forest | 98.25 | [43] | Pretrained CNN+ k-nearest neighbor | 98.95 | [44] | Ensemble model | 99.04 | Proposed | DenseNet-201+kNN | 100.00 |
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