[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Abstract

Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real‐time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource‐constrained devices. The internal architecture of the proposed model is inspired by the block‐wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small‐size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia’s dataset and our newly created small‐scaled fire detection dataset with extremely challenging real‐world images containing a high‐level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state‐of‐the‐art in terms of accuracy, false‐positive rate, model size, and running time, which indicates its robustness and feasible installation in real‐world scenarios.