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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates.

Ruizhen Gao1,2,3, Meng Chen1, Yue Pan1

  • 1School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

A new lightweight network, LGR-Net, efficiently detects small defects in elevator guide rail pressure plates. It achieves high precision and recall with reduced computational complexity, offering an effective solution for safety.

Keywords:
CBAMGhostConvMobileNetV3guide rail pressure platesmall defects

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Area of Science:

  • Mechanical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Elevator guide rail pressure plates are critical for stability, but defects can compromise safety.
  • Existing defect detection methods struggle with accuracy and efficiency, especially for small defects.

Purpose of the Study:

  • To develop a lightweight and accurate defect detection network for elevator guide rail pressure plates.
  • To address the limitations of current algorithms in localizing small defects and computational load.

Main Methods:

  • Proposed LGR-Net, a lightweight network based on YOLOv8n, incorporating MobileNetV3 and GhostConv.
  • Enhanced the network with a P2 layer for small object detection and CBAM for feature fusion.
  • Utilized data augmentation to create a custom dataset for training and validation.

Main Results:

  • LGR-Net demonstrated superior performance compared to other YOLO-series models.
  • Achieved high precision (98.7%), recall (98.9%), and mAP (99.4%).
  • Significantly reduced parameter count (2,412,118) while maintaining high accuracy.

Conclusions:

  • LGR-Net provides an efficient and effective solution for detecting defects in elevator guide rail pressure plates.
  • The network offers a balance of low computational complexity and high detection accuracy.
  • This advancement contributes to enhanced safety and maintenance in elevator systems.