# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 1 Introduction
One of the main divisions is related to the involvement of the data scientist and consists of the following four categories: supervised, unsupervised, semi-supervised, and reinforcement learning [8]. A further breakdown can be made according to the type of algorithm and its purpose. This division is broader than the previous one, but the most common examples include Decision Trees, Naive Bayes, kNN (k-nearest neighbors), SVM (Support Vector Machine), Rule-learners, and neural networks. The purpose of the latter is most often to analyze and segment images, as well as to detect and classify objects [9]. Neural networks are capable of replicating the actions of biological neurons communicating with each other in the brain, hence their name. The history of their development can be traced back to the second half of the 20 th century, more precisely to 1958, when [PERSON] presented the perceptron, considered to be the cornerstone of neural networks [10]. The year 1986 brought further developments as a group of researchers established the effectiveness of the backpropagation algorithm, which allowed for the creation of multi-layer networks capable of solving complex non-linear problems. The algorithm aimed to minimize cost functions by adjusting weights and biases, allowing the neural networks to learn new information based on the input data [11]. One of the most encouraging new approaches in this field at the moment is the YOLO (You Only Look Once) neural networks, which have been in continuous development since 2015 [12]. They are renowned for their ability to quickly and accurately analyze images and video in real time, outperforming previous detection algorithms. The core of their operation is based on detecting and classifying objects, assigning them to the appropriate type of class. These features have allowed YOLO to be used in many areas related to computer vision, such as surveillance systems. The potential of its use has also been demonstrated in many contexts related to the analysis of visual information, such as classifying crops [13; 14], cancer detection [15; 16], and pill identification [17]. Despite the wide range of applications of YOLO, the context of adapting graphics for the visually impaired has not been fully explored. The purpose of this article is to present the results of the research performed on the effectiveness of selected versions of YOLO networks in the context of removing redundant information, such as additional textual descriptions placed on images, and detectingelements in mathematical graphs and diagrams that are relevant from the point of view of a visually impaired person. These mentioned elements contained in the graphs include axes, linear functions, parabolas, hyperbolas, trigonometric functions, and polynomials [18]. The primary evaluation steps focused on the effectiveness of object recognition and classification, measured by the basic coefficients used in this type of network, i.e., the average accuracy, the error matrix, and the time needed to analyze an image. The results of the comparison will make it possible to identify the advantages and limitations of the selected versions of the YOLO network and thus indicate possible further directions for the development of a solution aimed at adapting graphs to the needs of blind people. The research performed will also constitute a first step towards the full automation of processes related to the preparation of exercises for visually impaired people, which may result in the creation of a stand-alone application to support current educational activities. The whole solution is aimed at developing cooperation between visually impaired and blind people, thus breaking down barriers in social and academic life, which has been shown to have a real positive impact [19].