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LSR-YOLO: A lightweight and fast model for retail products detection.

Yawen Zhao1, Mahmud Iwan Solihin1, Defu Yang2

  • 1Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia.

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This study introduces LSR-YOLO, a lightweight object detection model for retail AI. It significantly boosts inference speed and reduces computational cost for real-time applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning object detection enhances retail product identification.
  • Existing methods face challenges with high computational costs and slow speeds.
  • Need for efficient models in smart cities and intelligent devices.

Purpose of the Study:

  • Propose LSR-YOLO, a lightweight object detection framework based on YOLOv8n.
  • Optimize the model for deployment in robots and intelligent devices.
  • Improve inference speed and reduce computational load for real-time retail applications.

Main Methods:

  • Developed LSR-YOLO with architectural optimizations, including the CSPHet-CBAM attention module.
  • Implemented a channel pruning algorithm to reduce model redundancy.
  • Evaluated performance on the Locount and COCO datasets.

Main Results:

  • LSR-YOLO achieved 357.1 FPS inference speed on the Locount dataset.
  • The model reached mAP50 of 72.2% and mAP50-95 of 47.8%.
  • Demonstrated a 246.7 FPS increase over YOLOv8n with significantly fewer parameters and GFLOPs.

Conclusions:

  • LSR-YOLO offers superior accuracy and computational efficiency for real-time retail object detection.
  • The model's lightweight design and high speed make it suitable for resource-constrained devices.
  • Validated generalization ability on the COCO dataset, confirming its practical applicability.