Vision-based Real-Time Disaster Recognition Monitoring System using Raspberry Pi and Deep Learning Model

Natural disasters are destructive forces that greatly affect all people around the world. In the Philippines, earthquakes, tropical cyclones, and floods are some of the most frequent disasters that struck the country in recent years. Several studies utilizing technology have been conducted with varying degrees of success to reduce the impact of these uncontrollable events. Different from others, this paper investigates the use of a Deep Learning model deployed in a Raspberry Pi 3b for on-the-ground, real-time, automated disaster recognition and monitoring. It aims to empower emergency responders and people in the community to easily detect disasters as they happen in real-time, reducing the loss of life and damage to property. To this end, a novel low-cost monitoring system is proposed. Experiments and a survey made to emergency responders were conducted to validate the system’s feasibility and acceptability. Results revealed that the proposed system detects disasters with a high degree of performance. Also, it utilizes a low CPU and memory footprint while achieving seven frames per second processing rate during disaster recognition. In addition, the respondents find the system clear, helpful, innovative, and easy to use. Hence, the system is capable of recognizing disasters in real-time, proving acceptable and beneficial to people in the community.