Construction Site Equipment Dataset
Construction Site Equipment Data
- Labeling Type: Construction Site Equipment
- Data Format: Image
- Data Type: Synthetic Data
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$ 17,900
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About Dataset
1) Data Introduction
• Construction Site Equipment Dataset provides images and information on various types of equipment used on construction sites, including forms, PVC pipes, rubber cones, scaffolds, square manholes, steel bars, steel gratings, trench covers, water barriers, and flume tubes. The data is collected to facilitate real-time monitoring and activity tracking of equipment to measure and analyze work efficiency and productivity.2) Data Utilization
(1) Construction Site Equipment Data has characteristics that: • The dataset includes images and trajectory data of different types of construction equipment, making it useful for monitoring and analyzing their utilization and movements on site. • It provides detailed information helpful for understanding the usage patterns and operational characteristics of construction equipment. (2) Construction Site Equipment Data can be used to: • Development of real-time monitoring systems: The AI model can be trained to recognize and track various types of construction equipment, aiding in the automation of monitoring systems for construction sites. • Productivity measurement and analysis: By using this data, researchers and industry professionals can improve the accuracy and efficiency of productivity measurements and analyses, leading to better resource management and operational planning.Meta Data
Good
Outstanding
Data Samples 4
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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