Steel Defect Detection (Semantic Segmentation) Image Dataset
#semantic segmentation #defect detection #image classification #steel quality inspection #automated production line defect recognition #industrial manufacturing monitoring
- 500 records
- 1.6G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
In the field of industrial manufacturing, steel quality inspection is a crucial step in ensuring product quality. However, the current reliance on manual inspection methods poses challenges of inefficiency and inaccuracy, especially in large-scale production. Existing automated inspection systems still have shortcomings in defect recognition accuracy and flexibility. The construction of this dataset aims to support more precise and automated steel defect detection systems. The dataset is collected using high-resolution industrial camera equipment on the production line, ensuring sample diversity under different lighting and environmental conditions. The data collection process follows strict multi-phase annotation and consistency checks reviewed by an expert team with backgrounds in material science and non-destructive testing, ensuring high accuracy and consistency of annotations. Data is preprocessed before storage, including image enhancement, background noise removal, and stored in JPG format to facilitate processing and model training construction.
Dataset Insights
Sample Examples
75e1a356**.jpg|1079*1311|115.39 KB
4b4e6f74**.jpg|3024*4032|1.19 MB
d0e8dc7c**.jpg|1080*657|56.43 KB
1dfb0d8b**.jpg|3024*4032|1.02 MB
14b3ff98**.jpg|1080*657|98.19 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The specific type of defect on the steel surface, such as cracks, pits, scratches, etc. |
| defect_location | string | A detailed description of the defect location on the steel surface, which could be coordinates or area names. |
| severity_level | string | Describes the severity of the steel defect, such as minor, moderate, or severe. |
| surface_type | string | The type of surface of the steel, such as smooth, rough, etc. |
| image_quality | string | Describes the quality of the image, such as good, average, or blurry. |
| illumination_condition | string | Lighting conditions when the image was taken, such as bright, dim, or uneven. |
| camera_angle | string | Describes the angle of the camera when the image was taken, such as front, side, or top view. |
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 is the steel defect detection dataset?
- The steel defect detection dataset is a collection of images used to identify and segment surface defects in steel, aiding in the quality control of industrial production.
- Which industries are suitable for the steel defect detection dataset?
- This dataset is mainly used in the industrial manufacturing sector, particularly for steel production and its quality control processes.
- How to use the steel defect detection dataset for semantic segmentation?
- To use this dataset for semantic segmentation, deep learning models such as convolutional neural networks must be applied to identify and segment defective areas on the steel surface.
- What types of defects are included in the steel defect detection dataset?
- The dataset may include various surface defects such as cracks, dents, and scratches on steel.
- What are the benefits of using the steel defect detection dataset?
- Using this dataset can enhance the capability of automated defect detection in steel products, thereby improving product quality and production efficiency.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Steel Defect Detection (Semantic Segmentation) Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/5afa6fafc862c2554694189bf553d1b1},
urldate={2026-02-04},
keywords={steel defect detection, industrial image semantic segmentation, automated quality control, production line monitoring},
version={1.0}
}Using this in research? Please cite us.
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