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¹Department of Informatics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia. mmagdin@ukf.sk.

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Summary

This study introduces a Python application for object detection and classification in videos using YOLOv8. The system achieves 94.79% accuracy, demonstrating efficient real-time object identification.

Keywords:

CNN Classification of objects Neural network Object detection YOLO method

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Area of Science:

Computer Vision
Machine Learning

Background:

Object detection and classification are crucial in video analysis.
Previous YOLO versions have limitations in convolutional layers and real-time processing.

Purpose of the Study:

To design and program a Python application for implementing YOLOv8 for object detection and classification in MP4 videos.
To enable simultaneous determination of object location in time and frame.

Main Methods:

Developed a Python application utilizing the YOLOv8 algorithm.
Implemented a 5-layer convolutional network for 5 object classes.
Trained the network for 10 epochs.

Main Results:

Achieved an accuracy of 94.79% after 10 epochs.
Observed a declining error rate, reaching 0.15 after the 7th epoch.
Demonstrated a sufficiently trained network without further retraining.

Conclusions:

The developed application effectively implements YOLOv8 for real-time object detection and classification in videos.
The YOLOv8 model, with its enhanced convolutional layers, provides superior performance.
The system is capable of accurate and efficient object identification in video streams.