# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 2 Development of YOLO Neural Networks  
In machine learning, YOLO-type neural networks are currently an area of intense development. The basic premise of identifying objects in images and videos in real time has proven to be one of the most important elements in the solutions commonly used today. Among the many detection algorithms that have emerged over the years, these networks have a reasonable balance between the speed and accuracy of identified objects [20]. The development of the YOLO network began in the second decade of the 21 st century, and the first version was presented at CVPR 2016 (2016 IEEE Conference on Computer Vision and Pattern Recognition) by [PERSON] et al. [21]. It was the first approach to end-to-end object detection in real time. The novelty of the work presented is that a single image analysis using the network was sufficient to perform the classification, in contrast to previous solutions that required hundreds of runs to detect possible regions of interest and, in a second step, to classify the object in these regions. This characteristic gave rise to both the name and the acronym that would later be identified with this branch of the development of neural networks. The YOLO network has also simplified the outputs, basing them only on regression, in contrast to the Fast R-CNN [22], which used two outputs with classification parameters. The main architecture was based on 24 convolutional layers and 2 fully connected layers, but the authors also added a lightweight model with only 9 layers called Fast YOLO. Despite its novelty, the first version also had certain drawbacks, such as limiting the number of simultaneously detected elements of a given class to two objects in a grid, which made it difficult to detect neighboring objects of the same type. The result of the AP (Average Precision) measure, which, together with the AP\({}_{50}\) (Average Precision with a threshold of 0.5), is the main evaluation metric for YOLO networks, was 63.4% for the PASCAL VOC2007 [23] dataset, which contained 20 classes and was the benchmark used for comparisons at the time. The detailed architecture of this version was also presented in the aforementioned paper [21]. The second version, presented as part of CVPR 2017 (2017 IEEE Conference on Computer Vision and Pattern Recognition) by [PERSON] and [PERSON] [24], fixed some of the issues of YOLOv1 by introducing, among other things, features such as normalizing the batch to reduce overfitting, removing dense layers to make the architecture fully convolutional, improving the performance of the network on higher-resolution images, and introducing anchor boxes to be used as prototype shapes. In addition, changes were made to the training process of the network; for example, it was possible to use images of different sizes during the procedure, as the authors adapted the model to take random sizes as possible inputs. The fundamentals of the architecture were also changed resulting in using the Darknet-19 backbone with 19 convolutional and 5 max-pooling layers. All of these changes improved the average precision, with the YOLOv2 version achieving a score of 78.6% on the same PASCAL VOC2007 dataset. The authors also introduced a solution called YOLO9000, which was a combination of the COCO dataset with ImageNet classification data and a backpropagation training algorithm. The result was a network that was able to detect more than 9000 classes. The detailed architecture of this version was also presented in the mentioned article [24]. YOLOv3 [25] was published on ArXiv in 2018 by the same authors as the previous version, introducing further changes related to the performance of the network and its architecture. The changes included an improvement in the bounding box logic, which allowed a reduction in the number of anchor boxes assigned to an object during classification, and an enhancement of the class prediction to be able to identify multiple objects in the same box for complex images with labels that could sometimes overlap.