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Dataset of personal protective equipment.
1Department of Industrial Engineering and Management, National Chin-Yi University of Technology No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 411030, Taiwan (R.O.C.).
Data in Brief
|September 2, 2025
Summary
A new dataset for Personal Protective Equipment (PPE) target detection was created to enhance worker safety in high-risk industries. This resource aids in improving construction site security and industrial safety standards.
Area of Science:
- Computer Vision
- Occupational Safety and Health
- Machine Learning
Background:
- Personal Protective Equipment (PPE) is essential for worker safety in hazardous environments like construction and manufacturing.
- Existing safety measures require effective tools for monitoring and enforcing PPE compliance.
- Automated detection of PPE is crucial for real-time safety interventions.
Purpose of the Study:
- To develop and introduce a comprehensive dataset for Personal Protective Equipment (PPE) target detection.
- To provide a valuable resource for training and evaluating object detection models for PPE.
- To enhance the safety and security of workers in industrial and construction settings.
Main Methods:
- A dataset of 2286 images was curated from diverse public platforms and real-world scenarios.
- Images were annotated following the official YOLO (You Only Look Once) specification.
- The dataset is designed for compatibility with popular object detection frameworks like YOLOv8 and YOLOv11.
Main Results:
- A specialized dataset for PPE target detection has been successfully created.
- The dataset offers broad representation due to its varied image sources.
- Annotation in YOLO format ensures seamless integration with state-of-the-art object detection models.
Conclusions:
- The developed PPE dataset is a significant contribution to improving occupational safety.
- It facilitates the advancement of automated systems for monitoring PPE compliance.
- This resource is expected to lead to enhanced industrial safety standards and reduced workplace accidents.
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