## 1 Introduction
### Features of Extinguishing Fires
Features of the Application of the Acoustic Method for Detecting and Extinguishing Fires of Organic Substances  
A separate issue in the case of the use of classical fire protection solutions is the necessity of subjecting tanks containing the extinguishing agent to temporary pressure tests, which is not required in the case of the use of the acoustic technique (due to its use of the natural mechanisms of the propagation of acoustic waves for extinguishing flames). In this context, many features of the application of the acoustic method to detect and extinguish fires of organic substances acquire special significance. The use of acoustic waves appears not only as an effective method for extinguishing flames [80; 81; 82], but also as a method that is safer, cheaper to operate, and non-invasive to the environment, unlike classical firefighting agents. This arises from the absence of the chemical nature of acoustic waves, the lack of emission of harmful pollutants into the environment, and the absence of the formation of difficult-to-remove stains and deposits on the extinguished objects, which are indispensable for classical firefighting agents. Since water or chemicals are not used when extinguishing flames, the undoubted advantage of acoustic technology is its non-invasive nature in areas that are intolerant of water or where there is a high risk of damaging room components when using conventional fire extinguishing agents. As acoustic energy is clean (zero-waste technology) and leaves no environmentally harmful by-products, this is a major advantage. Furthermore, once the flames have been extinguished, one can quickly return to normal operations, which distinguishes this method from the techniques currently applied [83]. It is worth noting that acoustic fire extinguishers can use machine learning algorithms to detect flames (especially deep learning as a subdiscipline of machine learning). This integration of acoustic technology with modern vision techniques using, for example, deep neural networks (DNNs) allows one to increase the effectiveness of fire ignition extinguishing, not allowing it to spread, and to reduce the response time to a minimum (early response systems) [84]. Furthermore, information on flame detection can be immediately forwarded to relevant services. In practice, the effectiveness and speed of flame and smoke detection depend on the algorithms applied. In the case of embedded systems, based on the analysis performed, the modifications of the neural network and transfer learning, the MASK R-CNN (Region-based Convolutional Neural Network) and SSD (Single Shot Detector) MobileNet models have been shown to be highly effective [85]. Among other things, Mask R-CNN can be used to solve data segmentation problems by separating objects in videos or images. For SSD MobileNet, a high detection speed was achieved at the cost of recognition accuracy to find small objects, while Mask R-CNN recorded an approximately three times longer object detection time (this network can be successfully applied to detect multiple objects in an image) [85]. For that, the recognition accuracy for the second network was higher than for the first network (a difference of a few/several percent). Thus, the choice of an appropriate model is a trade-off between the accuracy and speed of detection. For the above reasons, both types of neural network are often used [86]. Since a lot of work has been carried out worldwide in recent years in terms of using and improving the effectiveness of multiple DNN architectures, one can expect continuous improvements in both the recognition accuracy and speed of flame and smoke detection. Neural networks make it possible to detect flames not only in forests or wilderness areas, but also in enclosed facilities. Their application also extends to places where, for example, due to high temperatures or dust, the use of classical sensors is severely hampered (e.g., foundries, sand dryers, heat treatment plants, etc.).