# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 1 Introduction  
The automatic and remote monitoring of the environment, vital signs and human behaviour has increasingly wider applications related to improving the quality of life and improving care for the elderly, sick or disabled [1; 2; 3; 4; 5; 6]. We can also use these solutions to increase the comfort of the life of patients or safety of people working in difficult conditions. Such systems can use different types of devices to observe and record parameters, phenomena and the surroundings. Those most commonly used are acceleration sensors, motion sensors, sensor networks, cameras and advanced vision systems using visible light cameras and thermosion. Typical threats to disabled or elderly people include falls, cuts, contusions and burns. Early detection of such situations can save the health or even the life of the injured person. Direct contact with high temperatures that are dangerous to health or life may be particularly burdensome in their consequences. The process of treating burns is usually difficult and long lasting, so it is worth preventing this type of accident. Such situations occur constantly in everyday life, and people with various diseases or health problems may become their potential victims. Since burns can occur at a temperature of 70 \({}^{\circ}\)C within 1 s [7], it seems reasonable to use a warning system that could protect people who are at risk. Taking into account the fact that burns occur extremely quickly, the article proposes an autonomous mobile system allowing for the early detection of danger andwarning against burns. There are several devices on the market that can be used to build such a system--Figure 1. The proposed system uses the Flir One Pro Pro [8] thermal imaging camera working with a smartphone. The applied mobile camera can record a thermal image directly in front of the user and transmit this information to the mobile device where the image analysis process, threat detection and warning take place. The Flir One Pro camera captures thermal images with resolution 320 \(\times\) 240 (images are scaled up) and a maximum temperature range of up to 400 \({}^{\circ}\)C. The device is equipped with its own battery and a built-in visible light camera with a resolution of 1440 \(\times\) 1080. The FLIR ONE cameras can be connected to Android and iOS devices via a micro USB or USB-C connector. They can record images at a frequency of eight frames per second. A better solution is to use integrated devices in the form of goggles with a built-in IR camera, but in this case, it is important that the equipment used has sufficient computing power or is able to communicate with mobile devices, as is the case with the Flir One Pro camera. The thermal imaging camera integrated with the goggles presented in Figure 1 (SATIR Thermal Vision 256) has an optical resolution of 256 \(\times\) 192 and a temperature range of \(-\)20 \({}^{\circ}\)C\(\rightarrow\)+550 \({}^{\circ}\)C. The set allows for communication in the 4G standard, which makes it possible to expand its capabilities and use external image analysis algorithms. Currently, however, it is not possible to directly use the device in the proposed system. To detect the risk of burns, images transmitted from a thermal imaging camera (in the form of a temperature matrix in degrees Celsius) showing the environment directly in front of the system user are used. This makes it possible for the application to react directly, based on the temperature value in the image, which may additionally determine the level of threat. The thermal imaging camera mounted on glasses or on the chest captures a thermal image directly in front of the user and transmits it to the mobile monitoring application that analyses the image and determines the level of threat. The algorithm implemented in it (discussed below) allows for the detection of arms or hands in the field of view of the camera and the detection of high temperatures (over 70 \({}^{\circ}\)C).