Analysis of methods for reducing the number of false alarms in video-based fire detection systems

• Autorzy: Mazur Michał

Identyfikatory

DOI

10.24136/jaeee.2023.005

• Języki publikacji: EN

Abstrakty

EN

The article focuses on a review and analysis of methods for reducing false alarms in video-based fire detection systems (VBFDS). The author of the article has designed a neural network and video-based flame detection algorithm to evaluate the effectiveness of methods found in the literature and other sources. The video-based flame detection algorithm was designed using a CIFAR-10-NET convolutional neural network. The D-Fire database, which contains 50000 fire images, was used to learn and test the algorithm. An error matrix was used to determine the effectiveness of the algorithm and methods to reduce the number of false alarms in video-based fire detection systems to determine parameters such as sensitivity (True Positive Rate, TPR), precision (Positive Predictive Value, PPV) and accuracy (ACC).

Słowa kluczowe

EN

false alarms; fire protection systems; neural networks; video-based fire detection

• Wydawca: Uniwersytet Technologiczno-Humanistyczny im. Kazimierza Pułaskiego w Radomiu
• Czasopismo: Journal of Automation, Electronics and Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 5, No. 2
• Strony: 15--22
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Mazur Michał

• Military University of Technology, Faculty of Electronics, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

Bibliografia

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