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26e5b8d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ================================================================================ PV PANEL DEFECT DETECTION DATASET ================================================================================ 📊 DATASET OVERVIEW -------------------------------------------------------------------------------- This dataset contains labeled images of photovoltaic (PV) panels categorized into six distinct classes based on their operational condition. It was curated as part of an educational and research initiative to evaluate and compare machine learning classifiers and hybrid deep learning approaches for automatic PV defect detection. By aggregating images from various open sources, this collection provides a structured, balanced, and high-quality dataset suitable for training robust classification models. 🔍 DATASET DETAILS -------------------------------------------------------------------------------- The dataset includes the following six classes: 1. Bird-drop : Panels contaminated with bird droppings, causing partial shading. 2. Clean : Panels in optimal, perfect condition with no obstructions. 3. Dusty : Panels accumulating dust, reducing light absorption efficiency. 4. Electrical-damage: Internal defects including hot spots, delamination, and bypass diode failures. 5. Physical-damage : External mechanical damage such as glass cracks or frame breakage. 6. Snow-covered : Panels partially or completely obscured by snow accumulation. 📊 DATASET DISTRIBUTION -------------------------------------------------------------------------------- The dataset is partitioned into Training, Validation, and Testing sets as follows: | Class | Training | Validation | Test | Total | |-------------------|----------|------------|-------|--------| | Bird-drop | 2,253 | 284 | 296 | 2,833 | | Clean | 2,189 | 271 | 278 | 2,738 | | Dusty | 2,097 | 264 | 258 | 2,619 | | Electrical-damage | 1,842 | 231 | 228 | 2,301 | | Physical-damage | 1,867 | 239 | 233 | 2,339 | | Snow-covered | 1,734 | 212 | 223 | 2,169 | |-------------------|----------|------------|-------|--------| | TOTAL | 11,982 | 1,501 | 1,516 | 14,999 | 🗄 SOURCES & ATTRIBUTION -------------------------------------------------------------------------------- 1. Primary Source (Kaggle): "Solar Panel Images Clean and Faulty Images" - Licensed for public and research use. 2. Supplementary Images: Manually collected from publicly available web sources (e.g., Google Images, educational portals, and academic references) to balance the class distribution. ⚠️ DISCLAIMER This dataset is a custom compilation intended strictly for non-commercial, educational, and research purposes. All rights to the individual images remain with their original authors or data providers. 💡 USAGE SUGGESTIONS -------------------------------------------------------------------------------- This dataset is ideal for: * Machine Learning model training and evaluation (SVM, Random Forest, etc.). * Deep Learning transfer learning experiments (ResNet, VGG, EfficientNet). * Developing Hybrid models (e.g., CNN for feature extraction + ML classifiers). * Experimentation with Explainable AI (XAI) methods (e.g., LIME, Grad-CAM). 👩💻 CITATION -------------------------------------------------------------------------------- If you use this dataset in your research or project, please cite it as follows: TechTrident (2025). PV Panel Defect Dataset for Machine Learning & Deep Learning Analysis. Available at: https://www.kaggle.com/datasets/alicjalena/pv-panel-defect-dataset |