Abstract: The generation of smoke is an early warning sign of a fire, and fast, accurate detection of smoke sources is crucial for fire prevention. However, due to the strong diffusivity of smoke, its morphology is easily influenced by environmental factors, and in complex real-world scenarios, smoke sources are often obscured. Current methods lack precision, generalization ability, and robustness in complex environments. With the advancement of deep learning-based smoke segmentation technology, new approaches to smoke source localization have emerged. Smoke segmentation, driven by deep learning models, can accurately capture the morphological characteristics of smoke. This paper proposes a precise and robust smoke source localization method based on deep learning-enabled smoke segmentation. We first conducted experimental evaluations of commonly used deep learning segmentation models and selected the best-performing model as input. Based on the segmentation results, we analyzed the diffusion characteristics and transmittance of smoke, constructed a concentration model, and used it to accurately locate the smoke source. Experimental results demonstrate that, compared with existing methods, this approach maintains high localization accuracy in multi-target smoke scenarios and complex environments, with superior generalization ability and robustness.

Yuanpan ZHENG, Zeyuan HUANG, Hui WANG, Binbin CHEN, Chao WANG and Yu ZHANG, “A Smoke Source Location Method Based on Deep Learning Smoke Segmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151144

@article{ZHENG2024,
title = {A Smoke Source Location Method Based on Deep Learning Smoke Segmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151144},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151144},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Yuanpan ZHENG and Zeyuan HUANG and Hui WANG and Binbin CHEN and Chao WANG and Yu ZHANG}
}