Research on the Method and Application of Truck Type Recognition Based on Deep Learning from the Perspective of Unmanned Aerial Vehicles

A lightweight detection model for truck models based on improved YOLOv5s (MobileNetV3-YOLOv5s) is proposed to meet the requirement for real-time detection under the limited embedded device resources carried by drones. Firstly, the authors use MobileNetV3 to replace the backbone feature extraction network and use deep separable convolution to replace traditional convolution to reduce the model’s parameter count. Secondly, the authors use DIOU loss as the regression loss of the bounding box to enhance the convergence speed of the model and improve the ability to fit data. Finally, the authors use the K-means clustering method to reset the prior box. The experimental results show that the mAP value of the improved model is 89.6%, which is 0.2 percentage points lower than before, but the volume is only 3.98MB, which is about half of the original model. The detection speed is also significantly improved compared with before the improvement. Therefore, the lightweight model based on improved YOLOv5s improves detection speed and significantly reduces model volume while ensuring detection accuracy. It enables efficient, real-time recognition of truck models under complex road conditions on embedded devices.

• Record URL:
  • https://doi.org/10.1177/03611981241254109
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • © The Author(s) 2024.
• Authors:
  • Sun, Wencai
  • 0000-0003-2431-7777
  • Jiang, Wei
  • 0009-0007-6281-9918
  • Liu, Hanwu
  • 0000-0002-0382-0552
  • Han, Lihong
  • Liu, Yang
  • Li, Shuzhao
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Drones; Machine learning; Trucks; Vehicle detectors
• Identifier Terms: YOLO
• Subject Areas: Data and Information Technology; Highways; Motor Carriers;

Filing Info

• Accession Number: 01925977
• Record Type: Publication
• Files: TRIS, TRB
• Created Date: Jul 30 2024 9:53AM