Federated learning based fire detection method using local MobileNet

Abstract

Fire is a dangerous disaster that causes human, ecological, and financial ramifications. Forest fires have increased significantly in recent years due to natural and artificial climatic factors. Therefore, accurate and early prediction of fires is essential. While significant advancements have been made in traditional and Deep Learning (DL) methods for fire detection, challenges remain in accurately pinpointing and recognizing fire regions, especially in diverse and large environments, to prevent damage effectively. To address these challenges, this paper introduces a novel Federated Learning (FL)-based method called Indoor-Outdoor FireNet (IOFireNet) for detecting and localizing fire regions. The proposed method incorporates a Bilateral Filter (BF) to effectively preprocess fire images to reduce noise artifacts and enhance detection clarity. It employs Super Pixel-based Adaptive Clustering (SPAC) to precisely segment fire and non-fire regions. A global IOFireNet model is developed to aggregate parameters from local models, improving detection accuracy across varied environments, while MobileNet is used for efficient data processing, enabling predictions on fire spread, severity, and affected areas to support early warnings. The proposed FL-based IOFireNet attains an accuracy rate of 98.65% for fire detection and 97.14% of mean IoU for segmentation. The proposed SPAC model reaches a mean IoU of 4.06%, which is 2.45% better than the graph cut algorithm and CRF model. The proposed model achieves an accuracy of 0.23%, 4.20%, 3.29%, and 10.02%, better than VGG-19, ResNet-50, Inception, and Dense Net, respectively.