Adaptive Fire System: Leveraging Image Analysis and Multi-Deep Learning Approaches for Dynamic Classification and Quality Assessment

Abstract

The major roles in the control and prevention of fire breakouts remain fire classification and quality evaluation. In this paper, a new methodology that integrates image analysis with multi-deep models such as ResNet50, MobileNetV2, and lightweight deep learning models for accurate fire classification and quality assessment is proposed. The proposed methodology intends to use deep learning models to extract meaningful features from fire images automatically. ResNet50 and MobileNetV2 are very prominent deep-learning architectures that have proven to perform well in image classification tasks. A lightweight convolutional neural network (CNN), targeted for fire analysis, is supposed to be computationally light but highly accurate. In such a context, concerning obtaining information about the fire’s intensity, spread, and danger, the proposed image analysis techniques come into play. Our approach provides a comprehensive evaluation of fire incidents by a combination of the outputs from the deep learning models and the image analysis results. The experimental results show that our proposed methodology was effective in fire classification and quality  valuation against ResNet50 and MobileNetV2. The proposed approach has huge potential for deployment in real-world applications involving fire prevention and control. It employs deep learning and image analysis techniques, hence providing a reliable and efficient way for fire classification and quality evaluation to ensure timely response and proper allocation of resources during fire emergencies.