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@article{164591,
        author = {Bhavika Paladugu and Dhanyashree P N and Sanjana S and Meghana S R and Dr. Manjunatha P B},
        title = {Effectively Managing Crowd in Disaster Areas using Deep Learning Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2786-2794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164591},
        abstract = {Disaster management in densely populated areas poses significant challenges, often requiring swift and coordinated response efforts. In this study, we propose a novel approach to enhance disaster response through the integration of drone-based surveillance and advanced deep learning techniques. Our system leverages state-of-the-art object detection models, such as YOLO(You Only Look Once), to monitor crowds in disaster areas in real-time. By accurately detecting and tracking individuals, our system provides critical information on crowd count and density, facilitating resource allocation and decision-making processes during crisis situations. Additionally, we incorporate thermal imaging technology to classify injuries based on temperature variations, enabling prompt identification and prioritization of medical assistance for affected individuals. Through autonomous crowd monitoring and injury classification, our project aims to improve the effectiveness and efficiency of disaster response efforts, ultimately reducing casualties and mitigating the impact of disasters on affected communities.},
        keywords = {},
        month = {},
        }