FIRE DETECTION USING COMPUTER VISION

Abstract

In urban environments, fire detection is essential for public safety, as rapid identification can prevent extensive damage and save lives. Traditional fire detection methods rely on smoke sensors, which often suffer from delayed response times and limited precision, particularly in complex environments. To address these limitations, this project introduces an advanced fire detection system that combines traditional image processing techniques with deep learning models, enhancing both accuracy and speed in detecting fires. The system uses Candidate Region Detection, Feature Extraction, and Classification stages, which incorporate multiple models and techniques, including Gaussian Mixture Models, Support Vector Machines, and fine- tuned deep learning models like ResNet50 and Inception V3. Candidate regions likely to contain fire are identified based on color and texture analysis. Fire-specific features are extracted using chromatic segmentation and texture analysis with Local Binary Patterns and Gray Level Co-occurrence Matrices. Additionally, flicker features are captured using Discrete Wavelet Transform to distinguish fire from other dynamic objects. The classification phase uses Support Vector Machines and fine-tuned CNNs to improve detection accuracy. This system’s adaptability and modularity enable it to be integrated with existing surveillance infrastructure, offering a robust solution for fire detection across various urban settings. Extensive testing shows a high level of accuracy and a significant reduction in false positives, making it suitable for real-world applications in residential, commercial, and public spaces. Future developments may involve integrating additional sensors and expanding the system's capabilities for more complex environments