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Computer vision-based safe distance-estimation method to prevent forklift tip-overs.

Aqsa Sabir1, Rahat Hussain2, Muhammad Sibtain Abbas2

  • 1Department of Computer Science and Engineering, Chung-Ang University, Korea.

International Journal of Occupational Safety and Ergonomics : JOSE
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a computer vision system to prevent forklift tip-overs by measuring the distance between forks and the load's center of gravity. The method achieved 93% accuracy in detecting unsafe conditions, enhancing workplace safety.

Keywords:
US OSHA rule compliancecomputer visionforklifthazard identificationtip-overworker safety

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Area of Science:

  • Engineering
  • Computer Science
  • Occupational Safety

Background:

  • Forklift tip-overs pose significant risks in construction due to improper load positioning.
  • Manual safety inspections are inefficient and require continuous human oversight.

Purpose of the Study:

  • To develop a computer vision-based system for early detection of forklift tip-over hazards.
  • To measure the critical distance between forklift forks and the load's center of gravity automatically.

Main Methods:

  • Utilized computer vision with YOLOv8 for object detection.
  • Developed a safe distance-estimation module (SafeDEM) integrating forklift length and safety regulations.
  • Classified behaviors based on US OSHA standards.

Main Results:

  • Achieved 93% classification accuracy in identifying safe/unsafe scenarios.
  • The system demonstrated real-time processing at 24 FPS.
  • Successfully tested with a 24-inch safety threshold.

Conclusions:

  • The proposed method offers a scalable, non-intrusive solution for enhancing forklift stability.
  • Enables early hazard detection before forklift movement, improving workplace safety.
  • Supports future dynamic deployment in addition to static operations.