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SMF-DETR: An Efficient Lightweight Detection Transformer for Real-Time Bearing Surface Defect Detection.

Min Gao1, Xiaoping Kang1, Kun Zhou2

  • 1Department of Mechanical and Electrical Engineering, Shanxi Institute of Energy, Jinzhong, China.

Annals of the New York Academy of Sciences
|November 30, 2025
PubMed
Summary

This study introduces SMF-DETR, an efficient algorithm for bearing surface defect detection. It significantly improves accuracy for small defects and reduces computational costs, enabling real-time industrial applications.

Keywords:
FDConvSMF‐DETRbearing defectsedge informationstar operation

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Area of Science:

  • Mechanical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Industrial equipment reliability depends on effective bearing surface defect detection.
  • Current deep learning methods face challenges with small targets, high computational load, and edge deployment.
  • There is a need for efficient and accurate defect detection algorithms suitable for industrial settings.

Purpose of the Study:

  • To propose an efficient defect detection algorithm, SMF-DETR, addressing limitations of existing methods.
  • To enhance accuracy for small bearing surface defects.
  • To reduce computational complexity and enable edge device deployment for real-time applications.

Main Methods:

  • Developed the StarNet-MEIS-FDConv-detection transformer (SMF-DETR) algorithm.
  • Incorporated element-level multiplication for high-dimensional feature mapping and reduced complexity.
  • Utilized a multiscale edge information selection mechanism for improved small defect detection.
  • Employed frequency domain dynamic convolution for efficient and adaptive feature extraction.

Main Results:

  • Achieved 96.2% mAP@50 and 98.1% accuracy on custom bearing defect datasets.
  • Outperformed baseline methods by 3.1% in mAP@50 and 2.9% in accuracy.
  • Reduced computational cost by 57.7% and model size by 37.1%.
  • Demonstrated real-time processing speeds (97.3 FPS on desktops, 58.1 FPS on RK3588).

Conclusions:

  • SMF-DETR offers a significant improvement in bearing surface defect detection accuracy and efficiency.
  • The algorithm is suitable for real-time industrial applications, including on embedded platforms.
  • Validation on public datasets confirms the algorithm's versatility and generalization capabilities.