Rossi Riccardo, Gelfusa Michela, Malizia Andrea, Gaudio Pasqualino
Department of Industrial Engineering, University of Rome "Tor Vergata", Via del Politecnico 1, 00133 Rome, Italy.
Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via di Montpellier 1, 00133 Rome, Italy.
Sensors (Basel). 2020 Nov 18;20(22):6602. doi: 10.3390/s20226602.
The early detection of fire is one of the possible applications of LiDAR techniques. The smoke generated by a fire is mainly compounded of CO, HO, particulate, and other combustion products, which involve the local variation of the scattering of the electromagnetic wave at specific wavelengths. The increases of the backscattering coefficient are transduced in peaks on the signal of the backscattering power recorded by the LiDAR system, located exactly where the smoke plume is, allowing not only the detection of a fire but also its localization. The signal processing of the LiDAR signals is critical in the determination of the performances of the fire detection. It is important that the sensitivity of the apparatus is high enough but also that the number of false alarms is small, in order to avoid the trigger of useless and expensive countermeasures. In this work, a new analysis method, based on an adaptive quasi-unsupervised approach was used to ensure that the algorithm is continuously updated to the boundary conditions of the system, such as the weather and experimental apparatus issues. The method has been tested on an experimental campaign of 227 pulses and the performances have been analyzed in terms of sensitivity and specificity.
火灾的早期检测是激光雷达技术的可能应用之一。火灾产生的烟雾主要由一氧化碳、水、颗粒物和其他燃烧产物组成，这涉及到特定波长下电磁波散射的局部变化。后向散射系数的增加会转换为激光雷达系统记录的后向散射功率信号中的峰值，这些峰值恰好位于烟羽所在位置，这不仅能检测到火灾，还能确定其位置。激光雷达信号的处理对于火灾检测性能的确定至关重要。设备的灵敏度足够高固然重要，但误报数量也要少，以避免触发无用且昂贵的应对措施。在这项工作中，一种基于自适应准无监督方法的新分析方法被用于确保算法能根据系统的边界条件持续更新，比如天气和实验设备问题。该方法在一次包含227个脉冲的实验中进行了测试，并从灵敏度和特异性方面分析了性能。
