IMPROVEMENT OF THE YOLOV8 OBJECT DETECTION MODEL BASED ON CHANGES IN THE MODEL'S ARCHITECTURE

Abstract

Object detection is one of the most prominent topics in deep learning due to its high applicability, ease of data preparation, and a wide range of practical applications.

Object detection is one of the most prominent topics in deep learning due to its high applicability, ease of data preparation, and a wide range of practical applications.

This research aims to improve the accuracy and speed of the Yolov8 application by changing in the model’s achitecture.