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Title	Low-Complexity High-Performance Smoke/Fire Detection System In Smart City Environments Using Cross-Attention, Capsule Based Optimized Siamese Convolutional Stacked Recurrent Neural Network
ID_Doc	35712
Authors	Ba Geri B.A.B.; Rabea O.A.Y.; Wang L.
Year	2024
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3673277.3673320
Abstract	Automatic, intelligent smoke and fire detection model is developed using an advanced deep learning (DL) algorithm with minimized complexity and improved detection performance for smart city environment. The proposed model of Cross-Attention Capsule based Optimized Siamese Convolutional Stacked Recurrent Neural Network (CACO-SCSRNN) is a combination of Siamese Convolutional Neural Networks (SCNN), Stacked Recurrent Neural Networks (SRNNs), and Namib Beetle Optimization Algorithm (NBOA). This hybrid model also includes fully connected (FC) layers for predicting change probability, cross attention to preserving the saliency correlation, and capsule vectors to acquire the location relationships of features. This hybrid DL model extracts all the spatial and temporal features from input images to detect the smoke and flame events through deep correlated features for early detection of the fire bursts. Evaluated over a real-world database, this proposed CACO-SCSRNN-based method enhances the smoke and fire images accurately with 99.12% accuracy, 100% precision, 97.89% recall, 98.93% F-measure, 0.91% FPR and 0.229 RMSE values, which are better than the existing methods. This ensures reduced model complexity and improves the reliability of fire-safety systems for smart city environments. © 2024 Copyright held by the owner/author(s).
Author Keywords	Fire Detection; Namib Beetle Optimization Algorithm; Siamese Convolutional Neural Networks; Stacked Recurrent Neural Network

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