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@article{196256,
        author = {J. Naresh Kumar and Shashank Tiwari and S. Manoj and Akshay Reddy and P. Jatin and Mr.Matru Dhanavarh},
        title = {Real-Time Construction Site Safety Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2002-2010},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196256},
        abstract = {Construction sites are inherently hazardous environ-ments where ensuring worker safety is a major challenge. Tra¬ditional monitoring systems rely on manual supervision, which is inefficient and prone to human error. This paper presents IntelliSafe AI, an intelligent system designed to automatically detect Personal Protective Equipment (PPE) compliance and hazardous situations using computer vision and deep learning techniques.
The proposed system integrates a Convolutional Neural Net¬work (CNN) for PPE detection with a YOLO-based object detection model for real-time hazard identification. The hy¬brid architecture improves detection accuracy while maintaining low latency. Experimental results demonstrate that the system achieves an overall accuracy of 97.8%, making it suitable for real-time deployment in construction environments.},
        keywords = {PPE Detection, Deep Learning, YOLO, CNN, Construction Safety, Hazard Detection},
        month = {April},
        }