Rice Cooker LCD Screen Damage Detection Dataset
#Image Classification #Defect Detection #Screen Inspection #Quality Control
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-03-22
AI Analysis & Value Prop
The current industrial landscape faces challenges with quality assurance, particularly in the inspection of electronic displays. Traditional methods often fall short in accurately detecting minute defects such as cracks and abnormal displays. This dataset aims to address these technical issues by providing a comprehensive resource for training models that can identify such defects with high precision. The dataset is structured with images collected from various rice cooker LCD screens, ensuring a diverse representation of potential defects. Data collection was performed using high-resolution cameras in controlled environments to maintain consistency. Quality control measures included multiple rounds of annotations and expert reviews to ensure data reliability. The images are stored in JPG format and organized in a structured directory for easy access.
Dataset Insights
Sample Examples
1d2060a3**.png|1280*1327|1.24 MB
3cc1f7eb**.png|1280*1316|2.52 MB
80bcf2bb**.png|1280*1517|1.21 MB
4e80ead0**.png|1280*1496|1.69 MB
db1c3f0e**.png|1280*1588|2.18 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| crack_count | int | Number of cracks on the rice cooker LCD screen. |
| crack_length | float | Length of the longest crack, measured in millimeters. |
| display_abnormality | boolean | Whether there is any display anomaly on the LCD screen. |
| damage_severity | varchar | Assessment of the screen's damage level, such as minor, moderate, or severe. |
| edge_damage | boolean | Whether there is noticeable damage in the screen edge area. |
| scratch_presence | boolean | Whether there are scratches on the screen surface. |
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 applications is the Rice Cooker LCD Screen Damage Detection Dataset suitable for?
- This dataset is suitable for applications such as electronic product quality inspection, smart manufacturing, fault prediction, and industrial automation.
- How does this dataset help improve the user experience of rice cookers?
- By detecting cracks and display anomalies in rice cooker LCD screens, this dataset helps identify and address product issues early, reducing negative user experiences during usage.
- What types of data does the Rice Cooker LCD Screen Damage Detection Dataset contain?
- The dataset contains images of rice cooker LCD screens with various types and degrees of cracks, suitable for object detection tasks.
- What should be considered when using the Rice Cooker LCD Screen Damage Detection Dataset?
- When using this dataset, attention should be paid to the quality of image data and the accuracy of annotations to ensure effective and reliable model training.
- What are the advantages of this object detection dataset?
- The advantage is its focus on specific applications in the industrial field, providing professional data support for rice cooker LCD display issues.
- How can the outcomes of the Rice Cooker LCD Screen Damage Detection Dataset be evaluated?
- The outcomes can be evaluated by measuring the accuracy, recall, and processing speed of detection models.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Rice Cooker LCD Screen Damage Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/68bccbedb81a23cd33e00b3cbe1a04e3?dataset_scene_id=2},
urldate={2025-08-28},
keywords={LCD screen damage detection,rice cooker inspection dataset,industrial quality control,image classification dataset},
version={1.0}
}Using this in research? Please cite us.
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