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CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation.

Fengjiao Liang1, Qingyong Li1, Xiaobao Li2

  • 1Key Laboratory of Big Data Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education, Beijing 100044, China.

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Summary

This study introduces a confidence-aware co-training (CAC) framework to improve weakly supervised crack segmentation (WSCS) by refining noisy pseudo-labels for better structural health monitoring.

Keywords:
co-trainingconfidence awarecrack segmentationpseudo-label dynamic divisionweakly supervised learning

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automatic crack segmentation is crucial for structural health monitoring.
  • Fully supervised methods require costly pixel-level annotations.
  • Weakly supervised crack segmentation (WSCS) faces challenges with noisy pseudo-labels.

Purpose of the Study:

  • To propose a novel confidence-aware co-training (CAC) framework for WSCS.
  • To address the issue of noisy pseudo-labels and improve segmentation model robustness.
  • To iteratively refine pseudo-labels for enhanced crack detection.

Main Methods:

  • A co-training mechanism with two collaborative networks to learn uncertain crack pixels.
  • A dynamic division strategy for pseudo-labels based on crack confidence scores.
  • Utilizing high-confidence pseudo-labels for parameter initialization and low-confidence labels for sample diversity.

Main Results:

  • The proposed CAC framework significantly outperforms existing WSCS methods.
  • Demonstrated effectiveness on Crack500, DeepCrack, and CFD datasets.
  • Achieved more robust crack segmentation with refined pseudo-labels.

Conclusions:

  • The CAC framework offers an effective solution for WSCS by managing noisy pseudo-labels.
  • This approach enhances the reliability and efficiency of structural crack detection.
  • CAC facilitates the development of more robust segmentation models for infrastructure maintenance.