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DV3-IBi_YOLOv5s: A Lightweight Backbone Network and Multiscale Neck Network Vehicle Detection Algorithm.

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This study introduces a novel vehicle detection algorithm balancing speed and accuracy for intelligent transportation. The proposed method achieves high detection accuracy and speed, suitable for mobile devices.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Vehicle detection is crucial for intelligent transportation systems, automatic driving, and urban planning.
  • Existing methods often struggle to balance the speed of lightweight networks with the accuracy of multiscale networks.

Purpose of the Study:

  • To develop a vehicle detection algorithm that integrates the speed of lightweight networks with the accuracy of multiscale networks.
  • To enhance feature processing and detection accuracy for vehicles of various sizes and categories.

Main Methods:

  • Utilized MobileNetV3 as a lightweight backbone network for enhanced detection speed.
  • Incorporated the ICAM attention mechanism to refine feature processing.
  • Integrated BiFPN and ICAM modules within the neck network for improved multiscale detection.

Main Results:

  • Achieved a detection accuracy of 71.19% on the Ua-Detrac dataset.
  • The algorithm has a parameter count of 3.8 MB.
  • Demonstrated a detection speed of 120.02 frames per second (fps).

Conclusions:

  • The proposed algorithm effectively balances vehicle detection accuracy and speed.
  • The results meet the requirements for embedded systems on mobile devices regarding parameter quantity, speed, and accuracy.