Solar Photovoltaics Panel for Dust Detection Dataset
Images of clean and dusty solar panels, allowing for the training of AI models that detect performance degradation caused by dust accumulation.
- Labeling Type: panel status
- Data Format: Image
- Data Type: Synthetic Data
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$ 7,200
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About Dataset
1) Data Introduction
• The Solar Photovoltaics Panel for Dust Detection Dataset is an image dataset designed to classify the presence of dust on the surface of solar panels. It consists of images of clean and dusty (dirty) panels.2) Data Utilization
(1) Characteristics of the Solar Photovoltaics Panel for Dust Detection Dataset: • The dataset contains images capturing the clean and dirty states of solar panels, which can be used to train AI models that detect performance degradation caused by dust accumulation. • The images were collected in outdoor environments, accurately reflecting the real-world conditions of solar power systems. (2) Applications of the Solar Photovoltaics Panel for Dust Detection Dataset: • Development of automated solar panel diagnostic models: The dataset can be used to train deep learning classification models that automatically determine the cleanliness of solar panels and predict appropriate maintenance timing. • Smart solar power plant monitoring systems: It can support the development of AI-powered monitoring systems that detect dusty panels in real time based on camera data collected from solar power facilities.Meta Data
Normal
Outstanding
Data Samples 5
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
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