## 3 Convolutional Neural Networks  
In the initial phase of the research, the CNN classifier was employed. Its role was to segment the image while simultaneously categorizing the identified objects and determining their precise positions on the image surface. We opted to use the well-known YOLOv4 - Tiny model for this purpose. The original version of this model is designed to detect and classify 80 different types of objects, none of which match our defined nine classes. As a result, we tailored the original model to suit our specific set of classes. This customization involved resizing the classes, allowing for the calculation of the probability of belonging to a given class within a set of nine classes as the output data. Subsequently, we conducted further training by using our prepared dataset (training set). The input data consisted of color images with dimensions of 416 \(\times\) 416 pixels. We employed supervised learning, and the model was trained over the course of 50 epochs. When configuring a machine learning model, precise parameter settings are essential to achieve the desired results. In the context of YOLOv4-Tiny or similar models for object detection, the following parameters and their values significantly influence the training process:  
Anchor sizes, defined as (10,14, 23,27, 37,58, 81,82, 135,169, 344,319) values, serve as reference points for object detection across various scales. These anchor sizes are specified as width-height pairs and are vital for accurately localizing objects of different dimensions. Strides parameter, defined as (16, 32), specifies spatial intervals, or strides, between anchor points at different scales. These strides are instrumental in ensuring that the model can effectively detect objects at various scales within an image. XY scaling factors represent fine-tuning predictions. These factors, such as (1.2, 1.1, 1.05), help adapt the model's predictions to account for variations in object sizes across the image. The anchor count per scale is defined as three. In this case, three anchors are designated per scale. This parameter plays a role in how the model approaches object detection at different spatial resolutions. The learning rate is a crucial hyperparameter for training machine learning models. The initial learning rate has been set to 0.001 (1 \(\times\) 10\({}^{-3}\)) while gradually decreasing it to 0.00001 (1 \(\times\) 10\({}^{-6}\)) over the course of training. Data augmentation introduces variations into the training data, enhancing the model's ability to handle diverse scenarios. We specified a warm-up phase consisting of two epochs. During this phase, the learning rate is adjusted gradually, facilitating smoother model convergence. Training occurs in two stages. The first stage spans 20 epochs while the second stage comprises 30 epochs. These stages guide the model through distinct training phases, potentially refining its performance. The Intersection over Union (IoU) Loss Threshold sets the threshold to 0.5. The IoU measures the overlap between predicted and actual object locations. Predictions with the IoU below this threshold result in increased training losses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Number** & **Name** & **Number** & **Number** \\  & & **Train Set** & **Test Set** \\ \hline Class 1 & pedestrian crossing & 2675 & 671 \\ \hline Class 2 & right arrow & 1567 & 390 \\ \hline Class 3 & left arrow & 1658 & 424 \\ \hline Class 4 & straight arrow & 2054 & 516 \\ \hline Class 5 & right–left–straight arrow & 198 & 51 \\ \hline Class 6 & right–straight arrow & 910 & 223 \\ \hline Class 7 & left–straight arrow & 879 & 218 \\ \hline Class 8 & left–diagonal arrow & 520 & 131 \\ \hline Class 9 & right–diagonal arrow & 223 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the training and test set sizes broken down by individual classes. In summary, these parameters and their values are pivotal in shaping the training process of the YOLOv4-Tiny model.