An efficient deep learning architecture for effective fire detection in smart surveillance

The threat of fire is pervasive, poses significant risks to the environment, and may include potential fatalities, property devastation, and socioeconomic disruption. Successfully mitigating these risks relies on the prompt identification of fires, a process in which soft computing methodologies play a pivotal role. Although, these fire detection methodologies neglected to explore the relationships among fire-indicative features, which are important to enable a model to learn more representative and robust features in remote sensing scenarios. In the context of small fire detection from aerial view using satellite imagery or unmanned arial vehicle (UAVs) presents challenges to capture rich spatial detail, hinder the model ability for accurate fire scene classification. Furthermore, it is significant to manage model complexity effectively to facilitate deployment on UAVs for fast and accurate responses in an emergency situation. To cope with these challenges, we propose an advanced model integrated a modified soft attention mechanism (MSAM) and a 3D convolution operation with a MobileNet architecture to overcome obstacles related to optimising features and controlling model complexity. The MSAM enabling the model to selectively emphasise essential features during the training process which acts as a selective filter. This adaptive attention mechanism enhances sensitivity and allowing the model to prioritise relevant patterns for accurate fire detection. Concurrently, the integration of a 3D convolutional operation extends the model spatial awareness, to capture intricate details across multiple scales, and particularly in small regions observed from aerial viewpoints. Benchmark evaluations of the proposed model over the FD, DFAN, and ADSF datasets reveal superior performance with enhanced accuracy (ACR) compared to existing methods. Our model surpassed the state-of-the-art models with an average ACR improvement of 0.54%, 2.64%, and 1.20% on the FD, ADSF, and DFAN datasets, respectively. Furthermore, the use of an explainable artificial intelligence (XAI) technique enhances the validation of the model visual emphasis on critical regions of the image, providing valuable insights into its decision-making process.