Today there is a revolution in the automotive industry. Cars are becoming self-driving with advanced sensors, cameras, and recognition algorithms. Algorithms and scenarios are evolving and improving every day, but it is too early for self-driving cars to go out on public roads. The work of recognition algorithms is far from ideal. For the correct and synchronized operation of all elements of an unmanned vehicle, a person needs to transfer all his intellectual experience to the computer systems of the vehicle. The developers are working to ensure that the car can see and understand what is happening around it. Such cars are already driving on the roads in test mode with a pilot, receiving a huge amount of information and learning. Already, tests and trials have proven that unmanned vehicles are safer than vehicles driven by people, and after mass implementation, the death rate on the roads will be reduced several times. Optical vision is an essential component of self-driving car due to the absence of human control. Speed, accurate detection of vehicles, empty parking, pedestrians, traffic signals, streets, and road signs can help self-driving vehicles drive safely and avoid mistakes and road accidents. On the other hand, object identification was challenging because objects in the physical world are affected by strong and low luminance, angularity, and scaling in image capture. The Convolutional Neural Network (CNN) approach was used by several researchers to improve object recognition outcomes in a variety of situations. However, the main disadvantage of this method is being unable to react quickly in real-time scenarios. In this work, the focus was on the YOLO model and the features of its work. Tests and comparisons with other state-of-the-art were given, which show that YOLO is the fastest model for real time objects recognition today. The architecture of the algorithm and the essential components on which the field experiment was conducted are described. Based on the results obtained, it was concluded that the YOLOv4 recognition model shows excellent results in terms of accuracy and speed.

Today there is a revolution in the automotive industry. Cars are becoming self-driving with advanced sensors, cameras, and recognition algorithms. Algorithms and scenarios are evolving and improving every day, but it is too early for self-driving cars to go out on public roads. The work of recogniti...