Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network

Author:

Rathnakumar RahulORCID,Pang YutianORCID,Liu YongmingORCID

Funder

Pipeline and Hazardous Materials Safety Administration

U.S. Department of Transportation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference44 articles.

1. What uncertainties do we need in Bayesian deep learning for computer vision?;Kendall,2017

2. Active boundary loss for semantic segmentation;Wang,2021

3. You only look once: Unified, real-time object detection;Redmon;Comput Vis Pattern Recognit,2016

4. Fast R-CNN;Girshick,2015

5. Faster R-CNN: Towards real-time object detection with region proposal networks;Ren;arXiv: Comput Vis Pattern Recognit,2015

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