Datasets:
file_name stringclasses 4 values | quality stringclasses 4 values | defect_type stringclasses 4 values | defect_location stringclasses 4 values | severity_level stringclasses 3 values | bounding_box_coordinates stringclasses 4 values | surface_condition stringclasses 4 values | light_condition stringclasses 1 value | image_quality stringclasses 2 values |
|---|---|---|---|---|---|---|---|---|
8422f40db2daec16658744c0586b7185.jpg | 1080*1414 | Cracks and wear | Slits and openings in the central part of the mold | Moderate | (150, 100, 300, 200) | Slight surface wear with metal shavings | Natural light | Clear |
a4e76f92f1ba140093566059c85336ed.jpg | 1080*1403 | Crack | Multiple slits in the central part of the mold | Medium | (100, 100, 200, 200) | Slightly worn | Natural light | Clear |
ddfdfae294b72f292dd207659a87d6a8.jpg | 1074*1888 | Notch | Edge of the circular hole | Moderate | (100, 150, 200, 250) | Relatively rough, with wear | Natural light | Relatively clear |
f4d4b8eee780f6c263646b3d7fe17f3d.jpg | 1920*1038 | No significant defect observed | None | None | None | Good | Natural light | Clear |
Mold Template Defect Recognition Dataset
In the industrial sector, mold quality inspection is crucial for ensuring product integrity but faces challenges such as inconsistent defect detection and high rates of false negatives. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error, leading to inefficiencies. This dataset aims to address these challenges by providing a comprehensive collection of labeled images that enhance machine learning models for accurate defect recognition in mold templates. Data was collected using high-resolution cameras in controlled industrial environments, ensuring clarity and consistency. Quality control measures included multiple rounds of labeling, consistency checks among annotators, and expert reviews to maintain high accuracy. The images are stored in JPG format, organized in directories by defect type, facilitating easy access and processing.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The types of defects appearing on the mold, such as cracks and pores. |
| defect_location | string | A detailed description of the defect's specific location on the mold. |
| severity_level | integer | The severity level of the defect as defined by the standards. |
| bounding_box_coordinates | string | Coordinates of the bounding box that marks the defect in the target detection task. |
| surface_condition | string | The overall condition of the mold surface, including the presence of oxidation or wear. |
| light_condition | string | The lighting conditions when capturing the image, such as natural light or artificial light. |
| image_quality | string | The quality assessment of the image, such as clarity or blurriness. |
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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