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ABSTARCT
Introduction: This research introduces a novel approach to early fire detection leveraging computer vision and machine learning techniques. Fires pose a significant threat to both lives and property, making early detection crucial for effective firefighting and damage mitigation.
Methodology: The proposed system employs computer vision algorithms to analyze real-time video footage for early signs of fire.
Data Acquisition: To enhance the model’s robustness, a comprehensive dataset featuring a variety of fire scenarios was compiled. so The dataset includes instances of different fire intensities, environmental conditions, and background complexities, ensuring the model’s adaptability in real-world situations.
Feature Extraction and Training: Key features are extracted from the input images through the CNN architecture, enabling the model to discern subtle details indicative of potential fire incidents. Training is conducted using an extensive dataset, allowing the algorithm to learn and generalize from various fire-related patterns.
Real-Time Monitoring: The developed system operates in real-time, continuously analyzing video feeds from surveillance cameras. Thus As the algorithm identifies potential fire-related patterns, it triggers immediate alerts, facilitating prompt response and minimizing response time.
Conclusion: hence This research presents a pioneering computer vision-based early fire detection system employing machine learning algorithms. The integration of advanced technologies facilitates swift identification of fire-related patterns, enabling timely response measures and enhancing overall fire safety. so The proposed methodology showcases promising results, paving the way for the development of robust fire detection systems with real-world applications.
