Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

Deep neural networks are effectively utilized for the instance segmentation of muck images from tunnel boring machines (TBMs), providing real-time insights into the surrounding rock condition. However, the high cost of obtaining quality labeled data limits the widespread application of this method....

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Bibliographic Details
Main Authors: Ke Lei, Zhongsheng Tan, Xiuying Wang, Zhenliang Zhou
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Symmetry
Subjects:
intelligent TBM tunneling
real-time muck analysis
self-supervised training
instance segmentation
fully convolutional masked autoencoder
HOG descriptor
Online Access:https://www.mdpi.com/2073-8994/16/2/222

Internet

https://www.mdpi.com/2073-8994/16/2/222

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