Datasets:
tags:
- Object Detection
- Image Classification
- Lamp Shade Inspection
- Panel Integrity Recognition
- Plastic Impact Detection
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
language:
- en
pretty_name: Transparent Cover Detection Dataset
size_categories:
- 1B<n<10B
Transparent Cover Detection Dataset
The current industrial sector faces significant challenges in ensuring the integrity of transparent components like lamp shades and panels. Existing solutions often lack precision and are inefficient in detecting subtle defects. This dataset aims to address these technical challenges by providing a robust resource for machine learning models focused on defect detection in transparent covers. The dataset consists of images collected using high-resolution cameras in controlled environments, ensuring clarity and detail. Quality control measures include multiple rounds of annotation, consistency checks among annotators, and expert reviews to validate the integrity of the labels. Data is stored in JPG format, organized by folders based on categories of defects, facilitating easy access and analysis. The dataset is designed to improve detection accuracy by providing high-quality, well-annotated images for training models.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The type of defect detected on the transparent cover plate, such as cracks, bubbles, scratches, etc. |
| defect_location | string | The specific location of the defect on the cover plate, usually expressed in coordinates. |
| defect_size | string | The size of the defect, usually expressed in terms of width and height. |
| defect_severity | string | A rating indicating the severity of the detected defect, which can be levels such as low, medium, or high. |
| detection_confidence | float | The confidence score of the defect detection result, ranging from 0 to 1. |
| image_quality | string | The quality grade of the image, which may include assessments such as clear, blurred, or high noise. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com