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Related Concept Videos

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Related Experiment Videos

YOLO-UTD: A Domain-Specific Detection Framework for Small Objects in UAV Traffic Surveillance.

Hailang Huang1,2, Meng Li1, Jiebao Zhang2

  • 1School of Civil Engineering, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces YOLO-UTD, a new detector for small objects in drone imagery, improving traffic surveillance. It enhances YOLOv8 to better detect small vehicles and objects in aerial views.

Keywords:
UAV visionYOLOv8feature pyramid networksmall-object detectiontraffic surveillance

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Detecting small, densely packed objects in drone aerial imagery presents significant challenges.
  • Existing object detection models often struggle with the scale and distribution of targets in UAV surveillance.

Purpose of the Study:

  • To develop a specialized small object detector, YOLO-UTD (YOLO-UAV Traffic Detection), for drone traffic surveillance.
  • To enhance the YOLOv8 framework for improved detection of small objects in aerial imagery.

Main Methods:

  • Introduced a specialized small-object detection head to replace the large-object head.
  • Integrated a shallow-augmented feature pyramid network (SFPN) for enriched semantic content in high-resolution features.
  • Incorporated a C2fA layer for adaptive fusion of spatial and semantic information using cross-attention.

Main Results:

  • YOLO-UTD achieved a 3.6% higher mean average precision (mAP) compared to YOLOv8 on the VisDrone2019 dataset.
  • Demonstrated a significant 5.3% increase in vehicle detection accuracy.
  • Maintained a low parameter footprint while enhancing detection capabilities.

Conclusions:

  • YOLO-UTD effectively addresses the challenges of small object detection in drone surveillance.
  • The model shows strong potential for smart city applications and advanced drone traffic monitoring.
  • The proposed enhancements significantly boost performance on fine-grained features and small targets.