IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8505-d1154202.html
   My bibliography  Save this article

A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial Images

Author

Listed:
  • Junli Liu

    (School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China)

  • Xiaofeng Liu

    (School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
    National & Local Joint Engineering Research Center for Intelligent Vehicle Road Collaboration and Safety Technology, Tianjin 300222, China)

  • Qiang Chen

    (School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
    National & Local Joint Engineering Research Center for Intelligent Vehicle Road Collaboration and Safety Technology, Tianjin 300222, China)

  • Shuyun Niu

    (ITS Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

Abstract

It is still a challenge to detect small-size vehicles from a drone perspective, particularly under low-brightness conditions. In this context, a YOLOX-IM-DeepSort model was proposed, which improved the object detection performance in low-brightness conditions accurately and efficiently. At the stage of object detection, this model incorporates the data enhancement algorithm as well as an ultra-lightweight subspace attention module, and optimizes the number of detection heads and the loss function. Then, the ablation experiment was conducted and the analysis results showed that the YOLOX-IM model has better mAP than the baseline model YOLOX-s for multi-scale object detection. At the stage of object tracking, the DeepSort object-tracking algorithm is connected to the YOLOX-IM model, which can extract vehicle classification data, vehicle trajectory, and vehicle speed. Then, the VisDrone2021 dataset was adopted to verify the object-detection and tracking performance of the proposed model, and comparison experiment results showed that the average vehicle detection accuracy is 85.00% and the average vehicle tracking accuracy is 71.30% at various brightness levels, both of which are better than those of CenterNet, YOLOv3, FasterR-CNN, and CascadeR-CNN. Next, a field experiment using an in-vehicle global navigation satellite system and a DJI Phantom 4 RTK drone was conducted in Tianjin, China, and 12 control experimental scenarios with different drone flight heights and vehicle speeds were designed to analyze the effect of drone flight altitude on speed extraction accuracy. Finally, the conclusions and discussions were presented.

Suggested Citation

  • Junli Liu & Xiaofeng Liu & Qiang Chen & Shuyun Niu, 2023. "A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial Images," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8505-:d:1154202
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8505/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/11/8505/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sudhir Kumar Rajput & Jagdish Chandra Patni & Sultan S. Alshamrani & Vaibhav Chaudhari & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Ahmed Saeed AlGhamdi, 2022. "Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zepeng Gao & Jianbo Feng & Chao Wang & Yu Cao & Bonan Qin & Tao Zhang & Senqi Tan & Riya Zeng & Hongbin Ren & Tongxin Ma & Youshan Hou & Jie Xiao, 2022. "Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction," Sustainability, MDPI, vol. 15(1), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8505-:d:1154202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.