yolov8

This paper presents an approach to improving mobile robot localization by filtering dynamic objects using camera image segmentation. The proposed algorithm integrates a Particle Filter with state-of-the-art computer vision techniques, specifically employing the YOLO model for segmentation, which effectively differentiates static elements of the environment from moving objects. This approach reduces the impact of noisy data and enhances localization accuracy in dynamic conditions, which is crucial for the reliable autonomous operation of mobile robots.

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 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.