## 4 Recent Developments in IRT Detection
### Classification of Objects in the IRT Image  
According to [99], classifying objects based on features is the most common way for recognizing and understanding image content. They also note that a considerable number of features such as color, object dimensions, or curve descriptions can be extracted from segmented objects to facilitate subsequent classification. Classification is usually implemented using artificial neural networks, of which there is a considerable number of types. For example, a widely used type is the back propagation neural network, which is a multilayer network consisting of an input layer, a hidden layer, and an output layer, where these layers are fully connected to each other, but the neurons of the same layer have no connection. Individual connections are defined by synaptic weights that can be adjusted as part of network learning [104; 115]. If a neuron's threshold is reached, i.e., if the sum of the products of the input values with the corresponding synaptic weights is equal to or greater than this value, the output signal of the neurons is produced by an activation function whose prescription is the same for all neurons in a single layer. Within multiple layers, the activation functions may differ, for example, in [72], a three-layer neural network with different activation functions was presented, applying a hyperbolic tangent function for the hidden layer and a linear function for the output layer. In contrast, both [PERSON] et al. and [PERSON] chose the same activation function, the standard (logistic) sigmoid function, for the hidden and output layers [104; 115]. [PERSON] used a Kohonen network containing fully connected layers, an input layer with a number of neurons equal to the number of image pixels, and an output layer with five neurons, to analyze the clusters in the image; the softmax function was chosen as the activation function [73]. The learning of the network is performed without a teacher and its main advantages include invariance to rotation and displacement. [PERSON] et al. and [PERSON] et al. focused their research on classification through convolutional neural networks (CNN), which are among the algorithms in the field of deep neural networks and are characterized by their ability to select features capable of finding the context between dependent and independent variables [60; 86]. According to [116], CNN are typically characterized by weight sharing and low link density, which leads to reduction in the number of parameters in network learning and also reduces the computational effort. Convolutional neural networks consist of three types of layers, convolutional, pooling, and classification. Convolutional layers are used to extract features from the image and are made up of feature maps of the same number as the number of features, i.e., if the feature maps have size \(M\times N\) and the number of features is \(k\), the size of the convolutional layer is \(M\times N\times k\). Each convolutional layer is followed by a pooling layer, which reduces the size of the convolutional layer in terms of the size of the feature maps, thus avoiding network overlearning. So, its size can be expressed as \(M/t\times N/u\times k\), where \(t\) and \(u\) are the number of sectors in the horizontal and vertical directions, respectively, which each convolutional layer is divided to. The classification layer takes the form of a fully connected network; its task is to recognize and categorize the detected objects in the image using the extracted features and their combinations from the previous layers [117; 118]. CNN can also be applied for image interpolation to higher resolution in super-resolution methods [62; 119].