• Dr Pavithra M
• Shanjay G
• Shiva Ashwath BR
• Sri Ragul Raj T

With the increasing frequency and severity of natural and man-made disasters, the ability to quickly locate and rescue survivors is paramount to saving lives. In this project, we propose an advanced AI-powered drone system designed to revolutionize search and rescue (SAR) operations by addressing the challenges faced by conventional rescue methods in complex environments. Traditional approaches often fall short in low-visibility conditions or hazardous terrains, where rapid and accurate detection of survivors is critical. Our solution integrates state-of-the-art drone technology with thermal imaging cameras, IoT connectivity, and sophisticated AI algorithms—including models such as YOLO and CNN—to detect and locate survivors in real time. The system operates by processing thermal video feeds onboard the drones, allowing for immediate analysis and identification of human heat signatures even in adverse weather or obscured visual conditions. Once a survivor is detected, the drone computes the exact geographic coordinates (latitude and longitude) and transmits this information through an IoT network to the central command center. This enables rescue teams to respond quickly and precisely, reducing the time between detection and intervention. Moreover, our design ensures seamless integration with existing emergency response infrastructures, providing dynamic, real-time data that enhances decision-making processes during critical missions. The robust IoT framework facilitates continuous communication between the drones and SAR teams, enabling adaptive routing and real-time situational updates. This interconnected approach not only improves the efficiency of rescue operations but also contributes to overall disaster management by providing comprehensive insights into the evolving scenario. The AI-powered drone system is particularly advantageous in environments where traditional rescue operations are hindered by debris, smoke, or darkness. By automating the initial search process and pinpointing survivor locations with high accuracy, our solution reduces the physical risk to human rescuers and accelerates the overall response time. Additionally, the incorporation of historical data analysis allows for improved prediction of high-risk zones, enabling proactive measures and optimized allocation of rescue resources in future operations. In summary, our project represents a significant leap forward in emergency response technology by leveraging the power of AI, drone technology, and IoT connectivity to enhance SAR operations. The system's ability to rapidly detect survivors and provide precise location data is poised to transform disaster management strategies, ultimately saving more lives and reducing the overall impact of disasters on affected communities.

AI, Drone, IoT, Thermal Imaging, YOLO, CNN, Search and Rescue, Disaster Management

"AI-Powered Drone System for Real-Time Survivor Detection in Disaster Zones", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.c496-c502, February-2025, Available :http://www.jetir.org/papers/JETIR2502259.pdf

"AI-Powered Drone System for Real-Time Survivor Detection in Disaster Zones", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppc496-c502, February-2025, Available at : http://www.jetir.org/papers/JETIR2502259.pdf