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

Gang WANG, Mingliang ZHOU*, Xuekai WEI, Guang YANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


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. We 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, we propose a detection algorithm that combines a deep learning network and a deduplication module for high-frequency detection. Finally, we 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 our proposed algorithm achieves an average accuracy of 99.57% and 53.11% on the two datasets, respectively. Additionally, our whole system achieves real-time detection and high-precision localization performance on real roads.

Original languageEnglish
Article number3296508
Pages (from-to)14254-14266
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number12
Early online date28 Jul 2023
Publication statusPublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.


  • Abandoned objects
  • deduplication module
  • deep learning
  • object detection


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