Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network
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DOI: 10.1016/j.ress.2023.109547
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References listed on IDEAS
- Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
- Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- McFarland, John & DeCarlo, Erin, 2020. "A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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- Cai, Yu & Zhao, Wei & Wang, Xiaoping & Ou, Yanjun & Chen, Yangyang & Li, Xueyan, 2024. "A novel multiple linearization method for reliability analysis based on evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
- He, Yu & Ma, Yafei & Huang, Ke & Wang, Lei & Zhang, Jianren, 2024. "Digital twin Bayesian entropy framework for corrosion fatigue life prediction and calibration of bridge suspender," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
- Rathnakumar, Rahul & Liu, Yongming, 2025. "Towards safer general aviation operations using a vision-based decision support system for weather threat avoidance," Journal of Air Transport Management, Elsevier, vol. 123(C).
- Zhang, Chen & Bahrami, Mahdi & Mishra, Dhanada K. & Yuen, Matthew M.F. & Yu, Yantao & Zhang, Jize, 2025. "SelectSeg: Uncertainty-based selective training and prediction for accurate crack segmentation under limited data and noisy annotations," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
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Keywords
Uncertainty quantification; Crack detection; Boundary refinement; Convolutional neural network; Bayesian deep learning;All these keywords.
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