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. The methodology includes the design and implementation of experiments to measure key metrics such as accuracy, speed, and resource utilization. Through meticulous analysis, this study aims to provide insights into the strengths and weaknesses of each model, facilitating informed decision-making for practical applications1. This paper presents the process of designing and conducting the performance analysis, highlighting the rationale behind the selection of specific technologies and methodologies. Furthermore, the study discusses the implications of the findings for future developments in target recognition systems. Keywords: yolov8, YOLO, OpenCV, NN model.