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Research on Pedestrian Detection Algorithm Based on MobileNet-YoLo.

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This study introduces MobileNet-YoLo, an efficient pedestrian detection model for small devices. It achieves higher accuracy and faster speeds than existing methods, enabling real-world applications.

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Large pedestrian detection networks are unsuitable for resource-constrained devices due to high computational demands.
  • Existing models like YoLov4-tiny face challenges with accuracy and speed on smaller platforms.

Purpose of the Study:

  • To develop an optimized pedestrian detection and recognition model, MobileNet-YoLo, for efficient deployment on small devices.
  • To enhance the accuracy and speed of target detection frameworks for mobile applications.

Main Methods:

  • Optimized YoLov4-tiny backbone using MobileNetv3 for feature extraction.
  • Implemented a Multi-scale Feature Fusion (MFF) model and Convolutional Block Attention Module (CBAM) for improved information processing.
  • Replaced standard convolutions with depth-wise separable convolutions to reduce model size.
  • Utilized k-means++ for anchor frame optimization and employed transfer learning for efficient training on limited datasets.

Main Results:

  • MobileNet-YoLo demonstrated superior performance compared to YoLov4-tiny, MobileNet-YoLov4, MobileNet-YoLov3, and YoLov5s.
  • Achieved an average mean accuracy improvement of 5.00% over YoLov4-tiny.
  • Significantly reduced model parameters and training time, addressing data scarcity issues.

Conclusions:

  • MobileNet-YoLo offers a viable solution for real-time pedestrian detection on small devices.
  • The proposed optimizations enhance detection efficiency and accuracy, paving the way for practical applications.
  • This model represents a significant advancement in mobile-based computer vision for pedestrian recognition.