Real-time fire detection and response system using machine vision for industrial safety

As industrialization accelerates, the risk and damage caused by fires in industrial settings have become increasingly severe. Current fire detection and response systems suffer from slow response times and inadequate accuracy, failing to meet the demands of modern industrial safety. This study presents the design and implementation of a real-time fire detection and response system based on machine vision. The system employs high-precision fire source recognition algorithms and intelligent control algorithms, utilizing cameras for real-time fire monitoring and deep learning techniques to accurately locate fire sources. Firefighting robots then promptly extinguish the identified fires. Experimental results demonstrate that the system achieves a fire source detection accuracy of up to 95% and an average response time of less than 3s in simulated industrial environments, significantly enhancing the intelligence and effectiveness of industrial fire protection. Furthermore, the system can automatically monitor and alert, transmitting fire information to relevant personnel in real-time, thereby providing robust technological support and assurance for industrial safety management. Moving forward, the research team will optimize existing algorithms and introduce new deep learning models to maintain high-efficiency fire detection performance in complex and dynamic industrial environments. Additionally, IoT integration and multi-sensor fusion will further enhance the system's monitoring and response capabilities. We will also explore the application of the system in actual industrial sites and study its feasibility and scalability in other high-risk environments.