Home/Image Dataset for Multi-Type Process Defect Recognition and Quality Assessment of Photovoltaic Cells
Image Dataset for Multi-Type Process Defect Recognition and Quality Assessment of Photovoltaic Cells
#Defect object detection #Defect classification #Image classification #Defect localization #Quality grading #Cell production line inspection #PV module incoming material screening #Manufacturing process issue tracing #Automated inspection system development #Production quality trend analysis
- 4500
- 0.7GB
- piece
- CATL
MOBIUSI INC
Updated:2026-11-05
AI Analysis & Value Prop
This dataset contains images of photovoltaic cells and corresponding annotation files for recognizing multiple types of manufacturing process defects and supporting cell quality assessment. Its records include image identifiers, image and annotation file paths, class IDs, image dimensions, and file formats, making the data easier to retrieve, organize, and use for model training. Typical applications include visual inspection on photovoltaic manufacturing lines, defect analysis, automated inspection system development, and quality control research.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| image_id | string | A unique identifier used to distinguish each image record. |
| image_path | string | The file path or location of the image. |
| label_path | string | The file path or location of the annotation associated with the image. |
| category_id | array | The identifier for the defect or other annotated class. |
| image_width | number | The image width, typically recorded in pixels. |
| image_format | string | The file format of the image. |
| image_height | number | The image height, typically recorded in pixels. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| 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 |
Frequently Asked Questions
- What does the dataset contain?
- The dataset centers on images and includes annotation file paths and class IDs, as well as image identifiers, dimensions, and formats. These records help organize the images and annotations for defect recognition tasks.
- Which models can this data support?
- It can support photovoltaic cell process defect classification, object detection, and localization, and may also be used to train or validate quality assessment models. Results depend on the annotation format, sample distribution, and preprocessing method.
- How can I obtain the data and check its license?
- Refer to the dataset product page for available access methods, delivery details, and license terms. Before use, check the permitted scope, applicable use cases, and whether commercial use is allowed; specific license terms are not provided here.
- What practical applications is the dataset suited for?
- It is suited to visual inspection on cell production lines, defect screening, and manufacturing process analysis. It can also support automated vision inspection systems and production quality control research. Validate it against the imaging conditions and defect distribution of the target production line before deployment.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Image Dataset for Multi-Type Process Defect Recognition and Quality Assessment of Photovoltaic Cells},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/57a73756da9b64b9c31c7bd75621dfbf},
urldate={2026-11-05},
keywords={photovoltaic cell defect dataset,solar cell defect recognition,PV image inspection,cell process defects,photovoltaic quality assessment,industrial vision inspection,defect detection training data,solar manufacturing inspection},
version={1.0}
}Using this in research? Please cite us.
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