# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 6 Conclusions
The burn risk detection efficiency (hand and hot object) reached 99.7%. Various configurations and structures of the neural network and various training and test set configurations for over 21,000 images were tested in the hand detection process. The obtained research results prove that it is possible to automatically and quickly analyze the user's environment and protect them against burns using the proposed deep learning methods and device sets. With further development and miniaturization of vision equipment, the proposed method can be used with more comfort for the user. Ultimately, the proposed CNN model will be implemented on Android mobile devices. For this purpose, the TensorFlow library can be used, which offers support for the Android system and allows for building and exporting trained CNN networks [34; 35]. The capabilities and hardware reserves offered by newer devices (fast multi-core CPUs with frequencies above 3 Ghz, GPUs clocked at over 1.4 GHz and specialized AI systems) and large memory capacity (up to a dozen GB) will allow for further expansion as well as the use of faster mobile thermal imaging cameras when they appear on the market. Hardware support for machine learning is increasingly offered in mobile devices (for example, Dimensiony 9300, Snapdragon 8 Gen 3, Dimensiony 8300 [36] processors contain specialized AI systems) and allows for the use of increasingly advanced solutions related to image processing and analysis. The proposed CNN system and architecture contribute to the development of the field of thermal image analysis by showing new possibilities offered by available mobile devices. Applications provided by camera manufacturers most often only enable temperature measurement pointwise or in a given area. The addition of artificial intelligence algorithms can facilitate reasoning, observation of phenomena or processes, and enable the automation of performed tasks, similarly to vision systems operating in visible light. Thermal images in vision systems are usually used less frequently than images in visible light, and it should be remembered that the results of the analysis of both types of images can also be combined, which most often improves the final effectiveness of these systems. Conceptualization, M.M. ; methodology, M.M. ; software, M.M. ; validation, M.M. and S.W. ; formal analysis, M.M. and S.W. ; investigation, M.M. and S.W. ; resources, M.M. ; data curation, M.M. ; writing--original draft preparation, M.M. ; writing--review and editing, M.M. and S.W. ; visualization, M.M., supervision, M.M. ; project administration, M.M. ; funding acquisition, M.M. and S.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Medical University of Silesia, grant number BNW-1-010/N/3/F. This work was supported by the National Centre for Research and Development, \"Intelligent system for effective analysis of diagnostic and repair work on industrial installations using mobile units and advanced image analysis--INRED\", project number: POIR.01.01.01-00-0170/17. **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Bioethics Committee of the Medical University of Silesia (PCN/CBN/0022/KB1/27/III/16/17/21). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. **Conflicts of Interest:** The authors declare no conflicts of interest.