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ICTD 2025

Artificial Intelligence (AI) in Transportation

POSTER RECEPTION (View list of posters here)

Comparative Case Study: Traffic Monitoring Using YOLOv11-Based Object Detection and Two Tracking Algorithms with Small Uncrewed Aerial Systems

Tuesday, June 10, 2025
4:45 PM – 6:00 PM MT
Abstract: Coherent traffic monitoring is pivotal for facilitating interstate and highway safety and efficiency. In such areas, the incorporation of small Uncrewed Aerial Systems (sUASs) coupled with computer vision (CV) and deep learning (DL) techniques are growing in popularity. Manual or sensor-based traffic monitoring systems often have limited coverage areas and are challenging to use for real-time traffic issue monitoring, while sUASs provide a wider view of traffic flow, faster deployment, and near-to-real-time monitoring of traffic patterns and vehicle tracking use state-of-the-art DL-based detection and CV-based tracking algorithms. This paper provides a case study with a comparative analysis of the performance of a YOLOv11-based object detection model integrated with two tracking algorithms, StrongSORT and ByteTrack, for tracking vehicles. These algorithms are analyzed using object detection and multiple object-tracking performance metrics. The findings of this study demonstrated a potential application of the studied algorithms in everyday vehicle tracking and traffic surveillance deployment.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Upon completion, participants will be able to describe the advantages of YOLO-based object detection in traffic monitoring.
  • Upon completion, participants will be able to compare the performance of DeepSORT, StrongSORT, and ByteTrack algorithms.
  • Upon completion, participants will be able to explain the benefits of utilizing sUAS for traffic monitoring