ADVANCED FIRE DETECTION USING INFRARED SENSORS AND NEURAL NETWORKS

Abstract

Video-based fire detection has emerged as a crucial research area for minimizing personal and property damage caused by fires. Early fire detection plays a vital role in disaster prevention, requiring high accuracy, low false alarm rates, and fast response times. This study integrates infrared sensing technology with convolutional neural networks (CNNs) to enhance fire detection accuracy and reduce false alarms. By leveraging data augmentation techniques and deploying the proposed model on embedded systems, the solution achieves real-time fire detection with improved performance compared to conventional algorithms. The results demonstrate the effectiveness of the approach in ensuring timely and reliable fire detection.