A Lightweight Fine-Grained VRU Detection Model for Roadside Units

Object detection of vulnerable road users (VRU) under low computing resources of roadside units is one of the key technologies to achieve vehicle-infrastructure cooperative perception. In this paper, a lightweight fine-grained VRU detection model is proposed. Analyzing the existing complex traffic environment, the traditional definition of VRU is no longer applicable. Our work includes two parts: One is to redefine the fine-grained VRU and construct a new dataset. This task makes the perceptual information obtained by detection more comprehensive and accurate. Another is to optimize YOLOv4 by using the channel pruning method in model compression. The optimized model is 60% lighter than the original model. Under the limitation of low computing resources at the roadside units, the real-time detection of VRU is realized while ensuring a certain detection accuracy.

• Record URL:
  • https://doi.org/10.1007/978-981-19-5615-7
• Record URL:
  • https://link.springer.com/chapter/10.1007/978-981-19-5615-7_21
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811956157
• Supplemental Notes:
  • © The Editors (if applicable) and The Authors, under exclusive license to Springer Nature Singapore Pte Ltd. 2023. The contents of this paper reflect the views of the authors and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
• Corporate Authors:

  Springer Singapore

  152 Beach Road
  Singapore,     189721
• Authors:
  • Shi, Jian
  • Sun, Dongxian
  • Zhang, Haodong
  • Tan, Haiqiu
  • Hu, Yaoguang
  • Wang, Wuhong
• Conference:
  • 12th International Conference on Green Intelligent Transportation Systems and Safety
  • Location: Beijing , China
  • Date: 2021-11-17 to 2021-11-19
• Publication Date: 2022-10-27

Language

• English

Media Info

• Media Type: Web
• Edition: 1st Edition
• Features: References;
• Pagination: pp 301-309
• Monograph Title: Green Transportation and Low Carbon Mobility Safety

Subject/Index Terms

• TRT Terms: Cyclists; Pedestrians; Proximity detectors; Traffic safety; Vehicle to infrastructure communications; Vulnerable road users
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01877761
• Record Type: Publication
• ISBN: 9789811956157
• Files: TRIS
• Created Date: Mar 28 2023 9:56AM