Enhanced Fire Detection and Precise Localization in Video Surveillance Systems Using Advanced Deep Convolutional Neural Networks
Keywords:
Video Surveillance Systems, Neural Networks, Heterogeneous Dataset, Low Computing Overhead, Surveillance Systems, Benchmark Fire Datasets
Abstract
This study focuses on enhancing fire detection and localization in video surveillance systems through advanced deep convolutional neural networks (CNNs). The primary objective is to improve the precision and efficiency of fire detection in CCTV footage while maintaining low computational overhead. By utilizing the GoogleNet architecture, the proposed model detects fire features and localizes fires more accurately, enabling faster emergency responses. The research was tested on benchmark fire datasets, with experimental results showing that the model outperforms existing fire detection methods in terms of accuracy and response time. This system offers enhanced reliability for fire detection, making it suitable for real-world applications in surveillance systems to prevent fire-related disasters.
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Published
2024-10-21
How to Cite
Regin, R., & Rajest, S. S. (2024). Enhanced Fire Detection and Precise Localization in Video Surveillance Systems Using Advanced Deep Convolutional Neural Networks. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 5(4), 401-412. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/674
Section
Articles
