Home Safety Hazard Classification Image Dataset

#image classification #object detection #risk prediction #home safety #intelligent security systems #risk assessment #home monitoring
  • 500 records
  • 1.5G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-10

AI Analysis & Value Prop

This dataset showcases outstanding advantages across multiple dimensions. Firstly, the annotation accuracy is extremely high, with each safety hazard image being reviewed and annotated by multiple safety experts to ensure over 99% consistency and completeness. Secondly, the dataset incorporates the latest AI-assisted classification technology in annotation methods and data processing, significantly improving annotation efficiency and quality assessment capability. In practical applications, this dataset can effectively enhance the recognition accuracy of intelligent security systems by at least 50%, thereby reducing the false alarm rate of safety hazards. Compared to other generic image datasets on the market, this dataset focuses on safety hazards in home environments, offering unique data scarcity. Additionally, the standardized design of data format and storage method makes it technically highly scalable and versatile, suitable for various machine learning frameworks and application scenarios.

Dataset Insights

Sample Examples

9e13ee49**.jpg|1860*3380|584.59 KB

Technical Specifications

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

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Cite this Work

@dataset{Mobiusiundefined,
  title={},
  author={Mobiusi},
  year={undefined},
  url={https://www.mobiusi.com/datasets/bc55d2006e302181faa8924b8b2260a6?dataset_scene_id=10},
  urldate={},
  keywords={},
  version={}
}

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