Yolo

SMART PARKING SYSTEM FOR LICENSE PLATE RECOGNITION BASED ON YOLO NEURAL NETWORK AND OPTICAL CHARACTER RECOGNITION

This paper describes a license plate recognition method, exemplified by training and deploying a machine learning model. The study uses the YOLO (“You Only Look Once”) neural network architecture and optical character recognition (OCR) techniques to extract license plate characters for real-time license plate recognition. Experimental tests, including model training, validation, and evaluation, demonstrate the effectiveness of these methods in enhancing automated access control in smart parking systems

Performance Evaluation and Optimization of Yolov8 Neural Network Models for Target Recognition

The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m, and YOLO models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, Python, and roboflow 3.0 FAST, the research aims to develop a robust methodology for assessing the performance of these NN models.

Advanced YOLO models for real-time detection of tomato leaf diseases

The increasing focus on smart agriculture in the last decade can be attributed to various factors, including the adverse effects of climate change, frequent extreme weather events, increasing population, the necessity for food security, and the scarcity of natural resources.  The government of Morocco adopts preventative measures to combat plant illnesses, specifically focusing on tomatoes.  Tomatoes are widely acknowledged as one of the most important vegetable crops, but they are highly vulnerable to several diseases that significantly decrease their productivity.  De

Performance Analysis of Different Types of Nn Models for Target Recognition

The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, the research is aimed at developing a robust methodology for assessing the performance of these NN models.

Method of Identification of Combat Vehicles Based on Yolo

A method for recognizing contours of objects in a video data stream is proposed. Data will be uploaded using a video camera in real time and object recognition will be performed. We will use the YOLO network – a method of identifying and recognizing objects in real time. Recognized objects will be recorded in a video sequence showing the contours of the objects.

Intelligent System of Constructing Vector Diagrams of Electrical Circuits

Phasor diagrams are a powerful tool for  visualizing and understanding the distribution of current, voltage, and power in electrical systems. During Russia's war against Ukraine, our energy industry has become very vulnerable to enemy attacks, and therefore needs a quick and effective recovery. Energy specialists lack software tools for working with the power system, and in the period of development of artificial intelligence, creating such tools is not so difficult.

Оbject recognition system based on the Yolo model and database formation

A system for recognizing objects that are captured in real time on a video camera in a noisy environment that changes to the surrounding conditions has been built. The method of filling the database for mobile military objects was studied. For object recognition, the YOLO v8 neural network is used, which allows you to track moving and identify objects that fall into the video from the video camera. This neural network makes it possible to track objects with a change in scale, during movement with obstacles.

An Alternative to Vending Machines

In this review article for a smart vending refrigerator, the contours of the future device are thought out and outlined and all its advantages are described. This device will be controlled using Computer Vision and some other features. The main control unit will be Raspberry PI, since it is the best for this device. Also, a web application was developed in which the user registers, and the applica- tion itself transmits the user's information through an API that will be developed to communicate with the web server, and the web server will store this information.

Methods and means for real-time object recognition accuracy increase in video images on iOS mobile platform

As a result of the analytical review, it was established that the family of Yolo models is a promising area of search and recognition of objects. However, existing implementations do not support the ability to run the model on the iOS platform. To achieve these goals, a comprehensive scalable conversion system has been developed to improve the recognition accuracy of arbitrary models based on the Docker system. The method of improvement is to add a layer with the Mish activation function to the original model. The method of conversion is to quickly convert any Yolo model to CoreML format.