Robot Welding Station Weld Image Dataset
#Image classification #defect detection #pattern recognition #Automated welding #industrial quality control #machine vision
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
In industrial manufacturing, the automation and quality control of welding processes pose important challenges. Currently, although some automated welding robot systems have been applied, they still show deficiencies in weld inspection and quality assessment, primarily reflected in the detection accuracy and real-time capability not meeting the requirements of high-quality manufacturing. This dataset aims to assist in developing smarter welding quality inspection algorithms through high-quality weld images to enhance production efficiency and welding quality. Data is collected using high-resolution industrial cameras and conducted in a standardized industrial environment to ensure the collected images are representative of reality. Images undergo multiple rounds of annotation and are reviewed by a team of welding experts to ensure the accuracy and consistency of annotations. The preprocessing process includes image denoising, enhancement, and cropping to improve model training effectiveness. The data is stored in a classified manner for quick retrieval and analysis.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| weld_type | string | The type of weld seam in the image, such as fillet weld, butt weld, etc. |
| weld_position | string | The specific location or orientation of the weld seam in the image. |
| defect_presence | boolean | Indicates whether there is a defect in the weld seam in the image, yes or no. |
| defect_type | string | If there is a defect, the type of defect, such as crack, porosity, etc. |
| weld_width | float | The width of the weld seam, measured in millimeters. |
| weld_penetration | float | The penetration depth of the weld seam, measured in millimeters. |
| weld_alignment | string | Indicates whether the weld seam maintains good alignment, such as good or poor. |
| surface_condition | string | The condition of the weld seam surface, such as smooth or rough. |
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 |
Frequently Asked Questions
- What can this dataset be used for?
- The robotic welding station seam image dataset can be used for researching and developing automated welding quality inspection systems to improve welding efficiency and quality.
- Which research areas is this dataset suitable for?
- This dataset is suitable for research in the field of industrial manufacturing, particularly applications related to automation, robotic welding, and computer vision.
- How to improve the accuracy of welding quality inspection using this dataset?
- The accuracy of welding quality inspection can be improved by refining machine learning algorithms, incorporating multi-modal data, and using higher quality images.
- What types of images does this dataset contain?
- The dataset contains seam images taken from various angles and under different lighting conditions at the welding station to facilitate comprehensive quality analysis.
- What are the potential challenges of using this dataset?
- Potential challenges include dealing with lighting variations, diversity in seam morphology, and image noise interfering with the inspection process.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Robot Welding Station Weld Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/e03ea1ea0d6bc736813988ee6ebf378f?dataset_scene_cate_type=5},
urldate={2026-02-04},
keywords={robotic welding, weld inspection dataset, industrial automation quality inspection},
version={1.0}
}Using this in research? Please cite us.
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