Year: 2024
Type: article
Abstract: In computer vision, object detection is a crucial task withapplications ranging from autonomous systems to surveillance. In this field, the You Only Look Once (YOLO) algorithm has become a major player, providing real-time detection with its unified architecture that manages object localization and categorization at the same time. This essay examines the development of YOLO, covering its early iterations as well as more recent developments likeYOLOv4. It looks at the algorithm's uses in a variety of industries, including driverless vehicles and imaging in medicine. Notwithstanding its achievements, problems still exist, and new approaches are being suggested by continuing study. The most recent additions to YOLOv4, such as CSPNet and PANet,represent a major improvement in scalability and performance. The impact of YOLO on object detection is highlighted in the paper's conclusion, along with its adaptability and the ongoing study that is required to solve problems and improve capabilities.
Authors: Ulykbek Amir, Azamat Serek, Magzhan Zhailau
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407195542