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MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios.

Yan Zhou1, Sijie Wen1, Dongli Wang1

  • 1School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China.

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|May 20, 2022
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Summary
This summary is machine-generated.

A new real-time object detection algorithm, MobileYOLO, enhances automatic driving systems. This algorithm improves detection speed by 70% and reduces model size significantly while maintaining high accuracy.

Keywords:
KITTI data setYOLOv4autonomous drivingobject detectionreal-time

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Object detection is crucial for autonomous driving systems.
  • Existing algorithms often struggle to balance detection speed and accuracy.
  • YOLOv4 is a popular object detection framework.

Purpose of the Study:

  • To develop a real-time object detection algorithm that improves both speed and accuracy for autonomous driving.
  • To reduce the computational complexity and model size of object detection systems.

Main Methods:

  • Proposed MobileYOLO algorithm based on YOLOv4.
  • Replaced feature extraction network with MobileNetv2 to reduce parameters.
  • Utilized depthwise separable convolutions in PAnet and head network.
  • Incorporated Efficient Channel Attention (ECA) module for feature fusion.
  • Introduced Single-Stage Headless (SSH) module for small object detection.

Main Results:

  • Achieved 90.7% accuracy on the KITTI dataset.
  • Reduced model parameters by 52.11 million compared to YOLOv4.
  • Decreased model size to one-fifth of YOLOv4.
  • Increased detection speed by 70%.

Conclusions:

  • MobileYOLO offers a significant improvement in real-time object detection for autonomous driving.
  • The proposed optimizations effectively reduce model size and enhance detection speed without compromising accuracy.