Vision Sensor Assisted Fire Detection in IoT Environment using ConvNext

Abstract

To mitigate social, ecological, and financial damage, effective fire detection and control are crucial. Performing real-time fire detection in Internet of Things (IoT) environments, however, presents significant challenges due to limited storage, transmission, and computational resources. Early fire detection and automated response are essential for addressing these challenges. In this paper, we introduce an IoT-supported deep learning model designed for efficient fire detection. The proposed model builds upon the pre-trained weights of the ConvNext convolutional neural network, which excels at detecting minute features and distinguishing between yellow lights and fire patterns. Implemented on an IoT device, the system triggers an alert when a fire is detected, prompting necessary actions. Our method, tested on the forest fire dataset, demonstrated a 4% improvement in accuracy compared to existing deep learning models for fire detection.