Automatic real-time fire distance, size and power measurement driven by stereo camera and deep learning

Wang, Zilong; Ding, Yifei; Zhang, Tianhang; Huang, Xinyan

doi:10.1016/j.firesaf.2023.103891

Cited by 13 publications

(1 citation statement)

References 19 publications

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“…The information on the power dissipated by the fire and its rate of change could be a critical parameter in its early stages, allowing a rough estimation of spread potential and the balanced allocation of fire extinguishing resources. Wang et al [90] developed an experimental set up based on a stereo vision camera system HBV-1780-2 S2.0 with the purpose of locating the flame in the image and estimating the HRR from scaling the fire image. The architecture of the system is presented in Figure 19.…”

Section: Foggia Dataset [45]mentioning

confidence: 99%

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Diaconu

2023

Fire

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Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

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Section: Foggia Dataset [45]mentioning

confidence: 99%