Scaffold Area Personnel Working at Heights Detection Dataset

#object detection #behavior recognition #image classification #working at heights monitoring #construction safety management #intelligent monitoring systems
  • 500 records
  • 1.5G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-05

AI Analysis & Value Prop

In the environmental meteorology industry, construction safety still faces many challenges, especially in working at heights. Existing systems rely on manual monitoring, which is inefficient and prone to false alarms and omissions. This dataset is dedicated to improving the efficiency and accuracy of safety monitoring through recognition algorithms. Data is collected on construction sites using high-resolution cameras, ensuring real and usable data. The quality of the data has been reviewed multiple times by experts, ensuring high annotation consistency. Annotators are professionals from the relevant industry field, with a team of over 20 members. Advanced image enhancement techniques are used for preprocessing, and the data is stored in JPG format, categorized by time and location. It emphasizes annotation accuracy and data integrity, has comprehensive data coverage and diversity compared to similar datasets, can support a wide range of application needs, and has high scalability.

Dataset Insights

Sample Examples

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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/ea59247852d55b3b35e8a90f3f186745?dataset_scene_cate_type=7},
  urldate={},
  keywords={},
  version={}
}

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