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Object Detection Based on Lightweight YOLOX for Autonomous Driving.

Qiyi He1, Ao Xu1, Zhiwei Ye1

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

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Summary
This summary is machine-generated.

This study introduces ShuffYOLOX, a lightweight model for autonomous driving that enhances target detection accuracy and response speed in complex scenarios. The improved model significantly boosts performance while reducing computational resources, making it ideal for driver assistance systems.

Keywords:
YOLOXattention mechanismautonomous drivinglightweight network designobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous driving systems require rapid and accurate target detection for safe operation.
  • Existing models often struggle with complex driving scenarios, leading to safety risks.
  • Lightweight and efficient models are crucial for real-time application in driver assistance systems.

Purpose of the Study:

  • To develop an improved object detection model for autonomous driving.
  • To enhance the accuracy and speed of target detection in complex scenes.
  • To create a lightweight model suitable for real-time applications.

Main Methods:

  • Proposed a novel lightweight feature extraction model, ShuffDet, to replace CSPDark53 in the YOLOX algorithm.
  • Integrated an attention mechanism into the Path Aggregation Feature Pyramid Network (PAFPN) to improve information focus.
  • Combined these enhancements into the ShuffYOLOX model for autonomous driving applications.

Main Results:

  • ShuffYOLOX achieved a mean average precision (mAP) of 92.20% on the KITTI dataset.
  • Reduced model parameters by 34.57% and GFLOPS by 42.19% compared to the original network.
  • Increased Frames Per Second (FPS) by 65%, indicating significant speed improvements.

Conclusions:

  • ShuffYOLOX offers a superior balance of accuracy and efficiency for autonomous driving.
  • The model's lightweight nature and enhanced performance make it highly suitable for real-time driver assistance systems.
  • This research contributes to the advancement of safer and more responsive autonomous vehicles.