Household Appliance Shell Crack and Minor Flaw Detection Dataset
#Image Classification #Object Detection #Anomaly Detection #Industrial Inspection #Quality Control #Defect Detection
- 15000 records
- 3.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-09-20
AI Analysis & Value Prop
In the current industrial landscape, ensuring the quality of household appliance shells is crucial to maintain safety and reliability. Manufacturers face challenges in detecting minute cracks and flaws that are not visible to the naked eye, which can lead to product recalls and safety hazards. Existing solutions often rely on manual inspections, which are time-consuming and prone to human error. This dataset aims to address the demand for automated detection of micro-defects, enhancing inspection efficiency and accuracy. The data is collected using high-resolution cameras in a controlled environment, where images of appliance shells are taken under various lighting conditions. Quality control measures include multiple rounds of annotation, consistency checks among different annotators, and expert reviews to ensure high accuracy. The dataset is organized in JPG format, with images stored in a structured directory for easy access and processing.
Dataset Insights
Sample Examples
bd8545a2**.png|1272*1500|606.74 KB
5e170ed0**.png|1161*1500|1.28 MB
2bbfd742**.png|1198*1500|1.16 MB
ff897e94**.png|1268*1500|1.25 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | Types of flaws on the appliance casing, such as cracks, scratches, or dents |
| defect_location | string | A general description of the flaw's location in the image |
| defect_severity | int | The severity level of flaws, typically graded based on the impact level |
| crack_length | float | The actual physical length of the crack, commonly measured in millimeters |
| crack_width | float | The actual physical width of the crack, commonly measured in millimeters |
| surface_texture | string | Description of the casing surface texture, such as smooth or rough |
| gloss_level | float | The glossiness of the surface, usually expressed as a percentage or specific numerical grade. |
| color_variation | string | Description of the color difference between the defect area and the normal area. |
| material_type | string | The materials used for appliance casings, such as plastic, metal, etc. |
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 is the Home Appliance Shell Crack Micro Defect Detection Dataset?
- The Home Appliance Shell Crack Micro Defect Detection Dataset is used to identify and detect small cracks and defects on home appliance shells to ensure product quality.
- In which fields is this dataset mainly applied?
- This dataset is mainly applied in the industrial sector, particularly in quality control during the manufacturing process.
- What data modalities are used in the dataset?
- The dataset uses image modalities for crack and defect detection.
- How does the dataset perform object detection?
- The dataset utilizes object detection techniques to identify and annotate small cracks and defects on appliance shells.
- Why is defect detection on home appliance shells important?
- Detecting defects on home appliance shells is crucial for ensuring consumer safety and enhancing product reputation.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Household Appliance Shell Crack and Minor Flaw Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/ea4277bf80f975da1996bb1df0c8e686?dataset_scene_id=2},
urldate={2025-08-28},
keywords={defect detection dataset,industrial quality control,image analysis,crack detection,appliance quality assurance},
version={1.0}
}Using this in research? Please cite us.
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