Year: 2024
Type: article
Abstract: The article presents problems related to the detection of fire phenomena using convolutional neural network techniques. The main issue described in the article focuses on determining the precision of flame detection depending on lighting conditions and the selection of CNN architecture. The types of neural networks tested are primarily SSD architectures, which, with their speed of operation and energy consumption, are the most common in mobile applications. The study shows which of the neural network architectures used have the highest average precision in detecting the fire phenomenon. The selection of networks under testing was analyzed in terms of the speed of the algorithm and its precision. Four pre-trained neural network models were used during the testing of two training bases. The complexity of each model directly affected the training time of the model, which oscillated between 2-8 [h], and the precision achieved.
DOI: 10.37105/iboa.236
Authors: Sebastian TATKO, Michał MAZUR
Institution: Military University of Technology in Warsaw
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405701654