Washing Machine Display Board Burn Detection Dataset
#Image Classification #Anomaly Detection #Failure Prevention #Fire Prevention #Quality Control
- 20000 records
- 3.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-13
AI Analysis & Value Prop
The washing machine industry faces significant challenges related to product safety, particularly concerning electrical failures that can lead to fires. Existing solutions often rely on manual inspection, which is inconsistent and not scalable, leaving a gap in proactive failure detection. This dataset aims to address the need for automated detection of burn marks and short circuit traces on display boards, thereby improving safety and reliability in industrial applications. The dataset comprises images collected from various washing machine display boards, captured under controlled lighting conditions to ensure clarity. Quality control measures include multi-round annotations, consistency checks, and expert reviews to maintain high accuracy. The images are stored in JPG format, organized by unique identifiers, and accompanied by metadata for each entry.
Dataset Insights
Sample Examples
1dac578b**.png|2797*1500|3.42 MB
9de84c08**.png|2253*1500|2.79 MB
3af570fd**.png|1957*1500|1.81 MB
44654860**.png|2295*1500|2.20 MB
f4178636**.png|2753*1500|2.62 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| burn_mark_location | string | Describes the specific location of the burn mark on the display board, such as the top left or bottom right corner. |
| burn_mark_shape | string | Describes the shape of the burn mark, such as circular or strip-shaped. |
| burn_mark_size | string | Describes the size of the burn mark, usually indicated by diameter or area. |
| burn_mark_color_intensity | string | Describes the intensity of the burn mark's color, such as light brown or dark brown. |
| burn_mark_texture | string | Describes the texture characteristics of the charred spot, such as smooth or rough. |
| circuit_damage_assessment | string | Evaluates the potential damage to the circuit function caused by the charred spot. |
| related_components | string | Describes the circuit board components related to the charred spot, such as capacitors and resistors. |
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
- How many images are included in the dataset?
- The dataset contains thousands of images focused on detecting burn marks and short circuits on washing machine display panels.
- What is the main application scenario of the dataset?
- The dataset is primarily used for industrial fault diagnosis, helping to detect burn marks and short circuits on washing machine display panels.
- What machine learning tasks is the dataset suitable for?
- The dataset is suitable for object detection tasks, particularly in the field of industrial equipment fault detection.
- How to use the dataset for object detection training?
- You can use the labeled image annotations along with modern deep learning frameworks like TensorFlow or PyTorch to train an object detection model.
- What are the industry impacts of the dataset?
- The dataset can significantly enhance industrial diagnostic efficiency, reduce human inspection errors, and provide essential data support for smart manufacturing.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Washing Machine Display Board Burn Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/cc6ba3e7f88ce2044046bf5c2053ad33},
urldate={2025-08-28},
keywords={washing machine dataset,burn detection,industrial safety,image dataset,quality control},
version={1.0}
}Using this in research? Please cite us.
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