# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 2 Related Works  
Since the main task of the proposed system is to warn of hand burns, hand detection is the most critical component of the system. Issues related to hand and arm detection are often discussed in the available literature in various applications. They are very often related to recognizing gestures [10], detecting location, tracking hands, building human-robot communication interfaces, controlling devices and systems or manipulating objects in 3D space [11; 12]. The typical solutions most often use independent image  
Figure 1: FLIR One mobile camera [8] and goggles with SATIR Thermal Vision 256 camera [9]. analysis methods (using RGB (conventional cameras), RGB-D (Kinnect--Microsoft, Leap Motion--[[https://leap2.ultraleap.com/products/leap-motion-controller-2/](https://leap2.ultraleap.com/products/leap-motion-controller-2/)]([https://leap2.ultraleap.com/products/leap-motion-controller-2/](https://leap2.ultraleap.com/products/leap-motion-controller-2/)) (accessed on 3 September 2024)) or IR (Flir One, [[https://www.flir.eu/products/flir-one-pro/](https://www.flir.eu/products/flir-one-pro/)]([https://www.flir.eu/products/flir-one-pro/](https://www.flir.eu/products/flir-one-pro/)) (accessed on 3 September 2024)) cameras) but also specialized equipment in the form of gloves communicating with a computer or robot. The appearance of depth sensors on the market has also resulted in 3D analysis methods [11] allowing for the assessment of hand location in three dimensions. Currently, methods using deep learning and convolutional neural networks are becoming more and more common [13]. The first group are methods based on the analysis of an image in the RGB or YCbCr palette. They use colour information for hand detection and localization which is the basis for further interpretation. In article [14], an image is initially mapped to the CbCr palette and next edge detection is performed using morphological operations. As a result, the algorithm locates areas that meet the colour criteria and designates areas of the hand. The proposed algorithm obtained the following results: TPR = 94.6% and FPR = 2.8%. The authors did not present the details of the set of studied images, which makes it difficult to assess the universality of the method. Another example of a hand localization method in visible light is the method described in paper [15]. Here, the authors proposed a fast, real-time classification method based on hand shape. The algorithm detects the hand area based on colour, determines the orientation of the hand in a vertical position and then uses shape context analysis, template matching, the orientation histograms, Hausdorff distance analysis and Hu moments to determine shape coefficients and classify them. The set contained 499 images and the method was used for gesture recognition. In recent years, with a significant drop in the prices of thermal imaging cameras and the rapid development of thermovision, the number of applications of this imaging method in many areas has been increasing. This opens up completely new possibilities and makes it possible to expand the methods related to visible light. Hand detection in thermal images makes it possible to eliminate problems typical for images in visible light, e.g., weak or uneven lighting, the impact of skin colour, complex coloured background. Exemplary solutions are based on the hand area model or information about the brightness in the analyzed area (model-based or appearance-based). The article [16] described a hand segmentation method based on statistical features of textures. The set of features included, among others, mean brightness, standard deviation, entropy, homogeneity and contrast. The areas designated in this way were further analyzed using the above-mentioned texture features, which allowed for the classification of regions located in the hand area. Image segmentation was performed using k-means cluster analysis. The algorithm was used in the treatment of rheumatoid arthritis. In paper [17], an adaptive hand segmentation algorithm was proposed.