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Updated: Aug 16, 2025

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Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects.

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  • 1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

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Summary
This summary is machine-generated.

This study introduces a hybrid compression method for foreign object detection on conveyor belts, significantly reducing model size and accelerating inference on edge devices. The optimized network achieves high detection accuracy while maintaining real-time performance, crucial for industrial safety.

Keywords:
belt conveyorcomputer visiondeep learningforeign object detectionlightweight CNNsnetwork compression

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Automation

Background:

  • Foreign objects on conveyor belts threaten operational lifespan and safety.
  • Edge computing requires efficient algorithms for real-time detection.

Purpose of the Study:

  • To develop a hybrid compression method for foreign object detection algorithms on edge devices.
  • To improve the real-time performance and accuracy of foreign object detection in conveyor systems.

Main Methods:

  • Implemented a hybrid compression technique integrating network sparsity, structured pruning, and knowledge distillation.
  • Utilized the Yolov5 network with three novel structured pruning strategies.
  • Introduced prior knowledge to enhance network resistance to motion blur.

Main Results:

  • Achieved >95% compression in network parameters, >90% in computation, and >90% in model size at a 0.9 pruning rate.
  • Demonstrated significant inference speedups: up to 70.3% on GPU and 157.5% on CPU.
  • Balanced real-time performance with >88.2% detection accuracy at pruning rates of 0.6-0.9.

Conclusions:

  • The proposed method effectively compresses foreign object detection models for edge deployment.
  • The optimized network ensures excellent real-time detection capabilities with minimal accuracy loss.
  • This research significantly contributes to the intelligent development and safe operation of coal mine equipment.