YOLOv8 All at Once

YOLOv8 is the latest object detection algorithm boasting fast speed and high accuracy.

In 2016, YOLOv1 was introduced by Joseph Redmon (University of Washington) and others through a paper titled "You Only Look Once: Unified, Real-Time Object Detection, CVPR" (https://arxiv.org/abs/1506.02640).

Subsequently, YOLOv8 has been released through the efforts of many developers and companies.

YOLO is suitable for real-time applications because it can detect objects at fast speeds. It detects and classifies objects while processing an image only once.

Multiple objects can be detected simultaneously in a single image, and overlapping objects can also be handled. However, there is a trade-off between accuracy and speed.

In this special lecture, we will examine how YOLOv8 operates, the future direction of real-time object detection technology, and case studies on how it can be applied.

If you are a developer, researcher, or student interested in object detection technology, do not miss this technical webinar!
 

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<Instructor Introduction>













Assistant Professor Lee Soo-an
Department of Computer Science, College of IT Engineering, Semyung University

A data scientist with the philosophy of making the world better through data. He takes interest in new research and technologies, and is particularly interested in artificial intelligence, machine learning, deep learning, big data, multidimensional analysis, recommendation systems, big data, and data visualization.

He has conducted research for 15 years in the fields of machine learning, deep learning, data mining, data warehouse, and database, and has accumulated over 33 years of development experience in in-memory databases, real-time stream data processing engines, and big data platforms.

• Teaching courses in artificial intelligence, computer vision, natural language processing, speech processing, big data analysis, etc.
• Data Intelligence Research Laboratory
• Industry-academy collaborative research
 

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