Object detection is one of the most important issues in computer vision, and it is a core technology in various computer vision-based systems. For example, face verification, autonomous driving, vehicle tracking, traffic analysis, and medical image analysis. Object detection systems can be more valuable when it applies to real-time application domain. This talk gives a brief introduction on real-time object detection methods for various devices. These methods include YOLOv4, Scaled-YOLOv4, and YOLOR.
Chien-Yao Wang received the Ph.D. degree in Computer Science and Information Engineering from National Central University, Zhongli, Taiwan, in 2017. He is currently a postdoctoral research fellow with the Institute of Information Science, Academia Sinica, Taiwan. His research interests include signal processing, deep learning, and machine learning. Currently, his research focuses on multi-task representation learning for multimodal signal. From 2020 to 2022, he releases several works on object detection and many computer vision tasks, including YOLOv4, Scaled-YOLOv4, and YOLOR. These works are the best real-time object detection methods in the world from 2020 until now.