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Lightweight Vehicle Detection Based on Improved YOLOv5s.

Yuhai Wang1, Shuobo Xu1, Peng Wang1

  • 1School of Information and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China.

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|February 24, 2024
PubMed
Summary

This study introduces a new lightweight YOLOv5 vehicle detection algorithm using integrated perceptual attention (IPA) and multiscale spatial channel reconstruction (MSCCR) modules. The improved algorithm reduces parameters by 9% while increasing detection accuracy by 3.1%.

Keywords:
artificial intelligencedeep learninglightweightobject detectionvehicle detection

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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Vehicle detection algorithms are crucial for intelligent traffic management.
  • Existing algorithms often face trade-offs between accuracy and computational cost.

Purpose of the Study:

  • To propose a lightweight yet accurate vehicle detection algorithm.
  • To enhance the YOLOv5 algorithm for improved traffic management systems.

Main Methods:

  • Developed a lightweight Integrated Perceptual Attention (IPA) module with a Transformer encoder.
  • Introduced a Multiscale Spatial Channel Reconstruction (MSCCR) module for efficient feature learning.
  • Integrated IPA and MSCCR into the YOLOv5s backbone network.

Main Results:

  • Reduced model parameters by approximately 9% compared to the original YOLOv5.
  • Increased average accuracy (mAP@50) by 3.1%.
  • Maintained computational complexity (FLOPS) without increase.

Conclusions:

  • The proposed method effectively reduces model parameters and enhances detection accuracy.
  • This lightweight YOLOv5 improvement is suitable for intelligent traffic management applications.