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

The construction industry faces significant risks from forklift tip-overs, often caused by improper load positioning that shifts the center of gravity beyond the stability triangle. Manual safety inspections are time-consuming and require constant human presence. To address this, the study proposes a computer vision-based distance-estimation method using forklift length to measure the critical distance between forks and the load's center of gravity. The system integrates forklift safety regulations, object detection via YOLOv8, the safe distance-estimation module (SafeDEM) and behavior classification based on US OSHA standards. Two safety scenarios were tested using a 24-inch safety threshold (if forklift length = 120 in). Our model achieved 93% classification accuracy (safe/unsafe) on 156 images, enabling early hazard detection prior to forklift movement. Although current testing focuses on static stock-point operations, the system's real-time speed (24 FPS) supports future dynamic deployment. This method offers a scalable, non-intrusive solution for enhancing machine stability and workplace safety.