Fire Detection and Autonomous Evacuation Guidance System

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception V3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system's ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system's capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use.