Datasets:
tags:
- object detection
- image classification
- product recognition
- automated warehousing
- robot navigation
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
language:
- en
pretty_name: Drawer Handle Detection Dataset
size_categories:
- 1B<n<10B
Drawer Handle Detection Dataset
In the current retail e-commerce industry, automated identification and handling of products are important means to improve efficiency. However, many existing object detection technologies have insufficient identification accuracy in complex environments, especially in the case of diverse product forms. Our dataset aims to address the recognition accuracy issue in object detection by providing high-quality images of drawer handles and their annotations. The dataset contains 5000 annotated images of drawer handles, captured in high resolution and in diverse environments. During data collection, professional photography equipment was used to shoot under both natural and artificial light to ensure image quality. On the quality control side, the annotations were subjected to multiple rounds of labeling and consistency checks to ensure their accuracy. Data is stored in JPEG format, organized as JSON files containing images and corresponding annotations.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| handle_type | string | Identifies the type of drawer handle, such as bar, recess, etc. |
| material | string | The material of the drawer handle, such as metal, plastic, wood, etc. |
| color | string | The color of the drawer handle, used for identification and classification. |
| texture | string | Describes the surface texture characteristics of the drawer handle. |
| orientation | string | The installation orientation of the drawer handle, such as horizontal or vertical. |
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