# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 6 Conclusions  
In summary, the main contributions of this research are the proposal of a deep learning-assisted automated method for burns detection, carrying out an analysis of the impact of several hyper-parameters on the model and proposing the best tuned model and a comparison with the state-of-the-art methods. High efficiency of the described method proves that it can be used as an element of a thermal threat warning system. Detection of a dangerous temperature is achieved with 100% accuracy. The hand and burn risk detection reaches Acc = 99.5% and Prec = 99.5%. Compared to previous studies and classic methods, it can be seen that the effectiveness has improved significantly by about 8-9%. Compared to current methods using CNNs, it can be observed that the effectiveness is comparable or better (Table 2). The use of deep learning and convolutional neural networks and optimization of the network structure also allowed for faster image analysis than in the case of the compared methods. The most important features of the proposed method are an independent and fully automatic hand detection block, high effectiveness and speed of action and classification of various threat levels. Given that the system will analyze the environment continuously, it can be assumed that the user will be warned of a hot object \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Method** & **Accu ACC** & **Precis PPV** & **Specif TNR** & **Sensi/Recall TPR** & **Image Count** & **Dataset** \\ \hline
1 & YCbCr [14] & ND & ND & 97.2\% & 94.6\% & - & Visible light images \\ \hline
2 & RGBD [21] & 97\% & - & - & - & 1080 & Visible light images \\ \hline
3 & SVM [19] & 89\% & 92\% & 87\% & 90\% & 5182 & Thermal images \\ \hline
4 & MS-FRCNN [26] & 94\% & - & - & - & 14,991 & Visible light images \\ \hline
5 & YOLOv7x [28] & - & 84.7\% & - & 79.9\% & 13,050 & Visible light images \\ \hline \multirow{3}{*}{6} & Proposed & \multirow{3}{*}{99.7\%} & \multirow{3}{*}{99.7\%} & \multirow{3}{*}{99.4\%} & \multirow{3}{*}{99.8\%} & \multirow{3}{*}{21,180} & \multirow{3}{*}{Thermal images} \\  & based on & & & & & & \\ \cline{1-1}  & [29, 31] & & & & & \\ \hline \multirow{2}{*}{7} & Proposed & \multirow{2}{*}{**99.5\%**} & \multirow{2}{*}{**99.5\%**} & \multirow{2}{*}{**99.2\%**} & \multirow{2}{*}{**99.7\%**} & \multirow{2}{*}{**21,180**} & \multirow{2}{*}{Thermal images} \\  & detector—optimized & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the effectiveness of hand detection methods with the proposed solution. and burn early enough, but a situation of direct risk of burns may only occur when a user does not react to previous warnings. The solution should be considered as a real-time system [32; 33] with hard real-time constraints due to the fact that the image analysis results should be delivered simultaneously with the recording of subsequent image frames, and exceeding the time limit may pose a health risk. A hot object is detected from a distance of over 4 m, so the user is informed about a potential threat in advance, which increases their vigilance. When recording an image at 8 frames/second, the system reacts to the appearance of a hand and a hot object in the frame within 0.125-0.25 s (first or second frame). If the user is about 0.5 m away from the hot object, touching the object may take about 1 s--so the user has about 0.5 s to react. However, if the user holds out their hand and the hot object is further away, the reaction time is longer. The method may have limitations in very small spaces where an object may suddenly appear in the field of view when the user turns towards a hot object that was previously not visible to the camera. When testing the developed model on a desktop computer using a GPU, the average classification time for 2218 test images was approximately 0.74 s using an Nvidia RTX 3060 graphics card and the CUDA library version 12.5. It can therefore be assumed that porting the model to a mobile platform should not prevent its use.