Vehicular Abandoned Object Detection Based on VANET and Edge AI in Road Scenes

Rapid processing of abandoned objects is one of the most important tasks in road maintenance. Abandoned object detection heavily relies on traditional object detection approaches at a fixed location. However, detection accuracy and range are still far from satisfactory. This study proposes an abandoned object detection approach based on vehicular ad-hoc networks (VANETs) and edge artificial intelligence (AI) in road scenes. The authors propose a vehicular detection architecture for abandoned objects to achieve task-based AI technology for large-scale road maintenance in mobile computing circumstances. To improve detection accuracy and reduce repeated detection rates in mobile computing, they propose a detection algorithm that combines a deep learning network and a deduplication module for high-frequency detection. Finally, they propose a location estimation approach for abandoned objects based on the World Geodetic System 1984 (WGS84) coordinate system and an affine projection model to accurately compute the positions of abandoned objects. Experimental results show that their proposed algorithm achieves an average accuracy of 99.57% and 53.11% on the two datasets, respectively. Additionally, the author's whole system achieves real-time detection and high-precision localization performance on real roads.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3296508
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Wang, Gang
  • Zhou, Mingliang
  • Wei, Xuekai
  • Yang, Guang
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 14254-14266
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Artificial intelligence; Detection and identification technologies; Highway maintenance; Mobile computing; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01906566
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 31 2024 9:13AM