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A Pedestrian Detection Network Model Based on Improved YOLOv5.

Ming-Lun Li1, Guo-Bing Sun1, Jia-Xiang Yu1

  • 1College of Electronics Engineering, Heilongjiang University, Harbin 150080, China.

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Summary
This summary is machine-generated.

This study introduces YOLOv5s-G2, a lightweight pedestrian detection network for autonomous driving. It enhances accuracy and reduces computational load, making it ideal for real-time applications.

Keywords:
Ghost modulesglobal attention mechanismlightweight modelloss functionpedestrian detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Pedestrian detection in autonomous driving faces challenges with high complexity and low accuracy in current methods.
  • Existing object detection models struggle with occluded and small pedestrian targets.

Purpose of the Study:

  • To propose a lightweight and accurate pedestrian detection network, YOLOv5s-G2, for autonomous driving systems.
  • To improve the efficiency and effectiveness of pedestrian identification, particularly for challenging targets.

Main Methods:

  • The YOLOv5s-G2 network integrates Ghost and GhostC3 modules to minimize computational costs.
  • A Global Attention Mechanism (GAM) module is incorporated to enhance feature extraction accuracy.
  • The α-CIoU loss function replaces GIoU loss for improved bounding box regression, especially for occluded and small targets.

Main Results:

  • The YOLOv5s-G2 network demonstrated a 1.0% increase in detection accuracy compared to the standard YOLOv5s.
  • A significant 13.2% reduction in Floating Point Operations (FLOPs) was achieved, indicating improved efficiency.
  • The network showed enhanced performance in identifying occluded and small pedestrian targets.

Conclusions:

  • The YOLOv5s-G2 network offers a superior balance of lightweight design and high accuracy for pedestrian detection.
  • This approach is well-suited for real-time autonomous driving applications requiring efficient and reliable object identification.