Authors: Mr. Suryaashokkumar Siriki, Miss. Savarapu Suhasini
Abstract: Rapid and accurate fire detection is essential for minimizing human casualties, reducing property damage, and enabling timely emergency response. Conventional fire detection systems primarily depend on smoke, heat, and gas sensors, which often experience delayed response, high false alarm rates, and limited effectiveness in complex or large-scale environments. Recent advances in deep learning and computer vision have enabled intelligent visual monitoring systems capable of identifying fire incidents directly from surveillance imagery. This paper presents a deep learning-based intelligent fire detection and early warning framework that employs Convolutional Neural Networks (CNNs) to automatically classify surveillance images into fire and non-fire categories. The proposed framework utilizes a comprehensive image preprocessing pipeline, including resizing, normalization, and data augmentation techniques such as rotation, scaling, zooming, and horizontal flipping to improve model robustness and generalization. Training optimization strategies, including Early Stopping and ReduceLROnPlateau, are incorporated to enhance learning stability and prevent overfitting. The performance of the proposed CNN model is compared with conventional machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), and AdaBoost, using evaluation metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the CNN-based framework achieves superior classification performance by effectively learning complex visual characteristics of flames and smoke while maintaining high detection accuracy and a low false alarm rate. The system further integrates an automated alert mechanism that instantly generates notifications upon fire detection, supporting rapid emergency intervention. The proposed framework provides an intelligent, scalable, and cost-effective solution for real-time fire monitoring and can be effectively deployed in smart buildings, industrial facilities, public infrastructures, and smart city surveillance systems to strengthen fire safety management and disaster prevention.
