Datasets:
file_name stringclasses 5 values | quality stringclasses 5 values | object_count stringclasses 3 values | object_type stringclasses 3 values | object_material stringclasses 5 values | defect_presence stringclasses 1 value | defect_type stringclasses 1 value | surface_texture stringclasses 3 values |
|---|---|---|---|---|---|---|---|
06913cfc17ec4ee1037845f936c662fd.png | 1323*1300 | 3 | Transformer | Plastic | No | None | Smooth |
2fa9a9618204d23acfe454275a683f86.png | 1030*1300 | 2 | Transformer, Inductor | Transformer: Metal, Inductor: Metal | No | None | Transformer: Smooth, Inductor: Rough |
8a81b9afc110113183dcbe7bb01eb3d0.png | 2541*1300 | 2 | Inductor, Transformer | Metal, Metal | No | None | Smooth, Rough |
9b1fe4768abc7649dd9094f1ef414d12.png | 1931*1300 | 8 | Transformer | Metal | No | None | Smooth |
f634eae21ca3f7f3dc649b7b6bdab8d7.png | 994*1300 | 2 | Transformer, Inductor | Transformer: Metal, Plastic; Inductor: Metal, Plastic | No | None | Smooth |
Inductor and Transformer Detection Dataset
The current industrial sector faces significant challenges in the accurate inspection of power modules and magnetic components, which directly impacts product quality and reliability. Existing solutions often rely on manual inspection methods that are time-consuming and prone to human error. This dataset aims to address the technical issue of automated defect detection in inductors and transformers by providing a rich set of annotated images that can be used to train machine learning models. The data is collected using high-resolution cameras in controlled environments to ensure consistency and quality. Quality control measures include multiple rounds of annotation, consistency checks, and expert reviews to maintain high accuracy. The dataset is organized in JPG format, each image accompanied by its corresponding labels and bounding box information, facilitating straightforward integration into machine learning workflows.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_count | int | The number of inductor and transformer targets in the image. |
| object_type | string | The detected target category, such as inductor or transformer. |
| object_material | string | The type of material on the target surface, such as metal, plastic, etc. |
| defect_presence | boolean | An indicator of whether there are defects on the target. |
| defect_type | string | The type of defect detected on the target, such as cracks, scratches, etc. |
| surface_texture | string | The texture characteristics of the target surface, such as smooth, rough, etc. |
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
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