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@article{179947,
        author = {ABINAYA K and SUBASHINI and Jegathiswari K and T Saranya},
        title = {CONSTRUCTION SAFETY MANAGEMENT USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {9035-9042},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179947},
        abstract = {Workplace safety in the construction industry is crucial, as non-compliance with safety regulations can lead to severe injuries and fatalities. Traditional manual safety inspections are inefficient, time-consuming, and prone to human error, especially on large construction sites. This project proposes an automated construction safety gear detection system using Deep Learning and Convolutional Neural Networks (CNNs) to enhance safety compliance. The system processes images of workers, detects essential Personal Protective Equipment (PPE) such as helmets, vests, gloves, and goggles, and triggers an alarm if any required gear is missing. By leveraging real-time object detection models like YOLO (You Only Look Once) and Faster R-CNN, the system ensures high accuracy even in complex environments with varying lighting conditions and worker movements. This automated approach reduces reliance on manual inspections, improves compliance with occupational safety regulations, and minimizes workplace hazards. Integrating this system with construction site surveillance enhances safety monitoring, prevents accidents, and fosters a safer work environment. The implementation of AI-driven safety enforcement marks a significant step toward the future of smart and automated construction site management.},
        keywords = {Personal Protective Equipment (PPE), Safety Gear Detection, Deep Learning Convolutional Neural Networks (CNNs), YOLO, Faster R-CNN, Object Detection, Workplace Safety, Real-time Monitoring, Automated Inspection and Smart Construction Sites.},
        month = {May},
        }