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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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An Infrared Image Defect Detection Method for Steel Based on Regularized YOLO.

Yongqiang Zou1, Yugang Fan1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

This study introduces a robust steel defect detection model using a Regularized YOLO framework with Coordinate Attention and Bi-directional Feature Pyramid Network. The enhanced model achieves high accuracy in identifying defects in infrared images for industrial applications.

Keywords:
YOLOv8cross-entropydefect detectioninfrared imageregularization

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Area of Science:

  • Materials Science and Engineering
  • Computer Vision and Artificial Intelligence
  • Non-Destructive Testing

Background:

  • Steel surfaces exhibit complex textures that can be misidentified as defects, necessitating advanced detection methods.
  • Accurate defect identification is critical for quality control in industrial steel manufacturing.
  • Existing defect detection models face challenges due to the subtle differences between surface textures and actual defects.

Purpose of the Study:

  • To develop a highly robust defect detection model for steel infrared images.
  • To improve the accuracy and reliability of automated defect identification in industrial settings.
  • To enhance feature extraction and fusion capabilities for superior defect recognition.

Main Methods:

  • A Regularized YOLO framework incorporating Coordinate Attention (CA) for enhanced feature extraction.
  • Integration of Bi-directional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion (BiFPN-Concat).
  • Regularization of the model's loss function to boost generalization performance.

Main Results:

  • The proposed model achieves a mean Average Precision (mAP@0.5) of 80.77% on the NEU-DET dataset and 99.38% on the ECTI dataset.
  • Demonstrates significant improvements of 2.3% and 1.6% over the baseline model, respectively.
  • The model maintains a low parameter count of only 3.03 million, indicating efficiency.

Conclusions:

  • The developed Regularized YOLO-based method offers a robust and accurate solution for steel defect detection using infrared imagery.
  • The integration of attention mechanisms and advanced feature fusion significantly enhances detection capabilities.
  • This approach is highly suitable for real-world industrial non-destructive testing applications.