Computer Vision for Safety Management in the Steel Industry

Abstract

The complex nature of the steel manufacturing environment, characterized by different types of hazards from materials and large machinery, makes the need for objective and automated monitoring very critical to replace the traditional methods, which are manual and subjective. This study explores the feasibility of implementing computer vision for safety management in steel manufacturing, with a case study implementation for automated hard hat detection. The research combines hazard characterization, technology assessment, and a pilot case study. First, a comprehensive review of steel manufacturing hazards was conducted, followed by the application of TOPSIS, a multi-criteria decision analysis method, to select a candidate computer vision system from eight commercially available systems. This pilot study evaluated YOLOv5m, YOLOv8m, and YOLOv9c models on 703 grayscale images from a steel mini-mill, assessing performance through precision, recall, F1-score, mAP, specificity, and AUC metrics. Results showed high overall accuracy in hard hat detection, with YOLOv9c slightly outperforming others, particularly in detecting safety violations. Challenges emerged in handling class imbalance and accurately identifying absent hard hats, especially given grayscale imagery limitations. Despite these challenges, this study affirms the feasibility of computer vision-based safety management in steel manufacturing, providing a foundation for future automated safety monitoring systems. Findings underscore the need for larger, diverse datasets and advanced techniques to address industry-specific complexities, paving the way for enhanced workplace safety in challenging industrial environments.

Keywords

• computer vision
• steel manufacturing
• hazard identification
• site monitoring
• deep learning