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RETRACTED: Recognition method of bridge apparent defects based on image processing and improved convolutional neural

Sheng Li1, Zhousheng Chang2, Xiaodan Zhou3

  • 1Innovation and Entrepreneurship Institute, Guangxi Normal University, Guilin, China.

Plos One
|November 14, 2025
PubMed
Summary

This study introduces an improved convolutional neural network model for accurate bridge defect detection. The new method enhances crack identification and size calculation, aiding bridge maintenance.

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Bridge appearance defect detection currently suffers from low accuracy and efficiency.
  • Automated methods are needed for reliable bridge health monitoring.

Purpose of the Study:

  • To develop an advanced bridge appearance defect recognition model.
  • To improve the accuracy and efficiency of detecting and analyzing bridge defects.

Main Methods:

  • Utilized image processing and an improved convolutional neural network (CNN).
  • Employed transfer learning for defect classification and recognition.
  • Integrated an improved fast region-based CNN for crack localization and segmentation.
  • Applied morphological theory for crack size extraction.

Main Results:

  • Achieved 98.2% detection accuracy, 0.6% missed detection rate, and 0.5% false detection rate.
  • Demonstrated a response time of 1.9s and 97.8% crack size calculation accuracy.
  • Showcased a 5.46% increase in positioning accuracy and a 0.11 increase in the area under the receiver operating curve compared to previous methods.

Conclusions:

  • The proposed model offers refined defect identification capabilities.
  • Provides a reliable foundation for routine bridge maintenance and health condition monitoring.