Research on a traffic flow statistical algorithm based on YBOVDT and SAM2.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

In the process of urbanization, traffic flow statistics are of great significance to traffic management. Existing traffic flow statistics solutions suffer from incomplete functionality and lack effective solutions for core issues. The closed-set object detection algorithms they employ can only perform detections based on fixed categories, which leads to limited recognition scope and weak model generalization ability.Moreover, the tracking algorithms used are unstable and have low computational efficiency. To address these challenges, this paper proposes a traffic flow statistical method based on YBOVDT(YOLO-World and BOT-SORT-Open Vocabulary Detection and Tracking)and SAM2.Specifically, in the method, this paper proposes a "Traffic Flow Data Processing and Analysis" module, aiming to optimize and supplement the five core functions required for traffic flow statistics tasks, thereby making the functions of the entire solution more comprehensive.In addition, this paper combines the latest open set object detection and tracking algorithms to enhance the recognition ability and tracking stability of traffic objects. In this study, a custom dataset was used to train existing traffic flow statistics models.The experimental results showed that the YOLO-World model achieved a precision of 76.99% and an mAP50 of 70.08%. A comparative analysis with YOLO-v3,YOLO-v5, YOLO-v6,and YOLO-v8 algorithms indicated that, while balancing spatial and temporal resource consumption and accuracy, the proposed algorithm offers higher recognition accuracy and environmental adaptability. The experimental results further validated that this method demonstrates significant improvements in handling traffic flow statistics tasks in complex traffic environments.
(© 2025. The Author(s).)

Declarations. Competing interests: The authors declare no competing interests.

IEEE Trans Pattern Anal Mach Intell. 1986 Jun;8(6):679-98. (PMID: 21869365)

No. 23YJAZH034 This work was supported by Ministry of Education Humanities and Social Science Research Project; No. 2024-AFCEC-056,2024-AFCEC-057 National Computer Basic Education Research Project in Higher Education Institutions; No. Z421A24315,Z421A22349 Enterprise Collaboration Project

Keywords: Multimodal learning; Traffic flow statistics; Transportation engineering

Date Created: 20250530 Latest Revision: 20250602

20260130

PMC12125392

10.1038/s41598-025-04336-2

40447809