An early warning AI-powered portable system to reduce workload and inspect environmental damage after natural disasters (2104.00876v1)

Abstract: 1.3 million household fires, 3,400 civilian deaths, and 23 billion dollars in damage, a fire department is called to respond every 24 seconds. Many firefighters are injured during search and rescue operations due to hidden dangers. Additionally, fire-retardant water runoff pollution can threaten human health. My goal is to develop a system to monitor calamity-induced environment damage to provide early-intelligence to incident-commanders. I have developed a multi-spectral sensing system to inspect air and water quality for safer and accessible hazardous environment operations. Key components include a) drone mounted with four sensors (gas sensors, thermal camera, GPS sensor, visual camera) and wireless communicator for inspection, b) AI-powered computer vision base-station to identify targets, c) low-cost, portable, spectral water quality analyzer and d) robotic retriever. The prototype demonstrates the potential for safer and more accessible search and rescue operations for fire-fighters and scientists. The gas sensor could identify thick smoke situations (thresholds > 400). The visual and thermal cameras detected hidden hot objects and sent images to AI-powered analyzer to identify and localize target with rescue GPS coordinates for robotic retrieval. Water quality was analyzed with spectral signatures to indicate turbidity levels that correlate with potential pollutants (threshold > 1.3). Prototype results were shown to the Sunnyvale fire department and received encouraging feedback. Future goals include monitoring firefighter health and overexertion with smart clothes.