Seeing Safety: Computer Vision for Real-Time PPE Monitoring in the Middle East Construction Sector

Abstract

Workplace safety is still a major concern worldwide, especially in high-risk settings like construction sites where manual PPE monitoring is insufficient. As employers want to move towards a more efficient and safer worksite, context awareness is becoming increasingly important in the construction industry. Besides, conventional PPE compliance checks can be ineffective, costly and prone to errors. This paper presents a novel approach to enhance workplace safety in construction environments through the development of an automated Personal Protective Equipment (PPE) detection system utilizing advanced computer vision techniques. YOLOv5 object detection models were used in this study to address the limitations of manual PPE compliance monitoring by achieving real-time detection under diverse environmental conditions, such as variable lighting and occlusion. The methodology encompasses comprehensive dataset preparation, annotation, and model training, achieving a mean Average Pre-cision (mAP) exceeding 70%, with YOLOv5m(a) attain-ing an mAP@0.5 of 0.841. This research contributes to reducing workplace hazards by improving monitoring efficiency and lays a foundation for future advancements in industrial safety systems.