Abstract: Flood disasters often result in large-scale human displacement, making timely detection and rescue operations critical. Conventional ground-based approaches for locating stranded individuals are often time-consuming, labor-intensive, and limited by accessibility challenges. To overcome this limitation, the project combines an ESP32-CAM module with a drone system to provide lightweight aerial monitoring and real-time detection of individuals during flood emergencies. By deploying a lightweight version of the YOLO (You Only Look Once) object detection model, the ESP32-CAM processes live video streams and identifies individuals from a top-view perspective. The captured feed is transmitted via Wi-Fi and monitored through a laptop browser, providing rescuers with an efficient tool for rapid assessment. The system can detect people entering or exiting the frame and automatically count the number of individuals, offering valuable situational awareness. Additionally, communication between operators and the drone is supported via a walkie-talkie system to enhance coordination during rescue missions. This solution emphasizes portability, affordability, and real-time processing, making it suitable for disaster-prone regions with limited infrastructure. In summary, the proposed system seeks to connect drone-based monitoring with AI-powered disaster management by delivering a dependable, cost-effective, and fast-response solution for flood rescue operations.

Keywords: ESP32-CAM, YOLO, Flood Disaster, Drone, Person Detection, People Counting, Aerial Surveillance, Real-Time Monitoring.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14411