Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation
Ke Lei,
Zhongsheng Tan,
Xiuying Wang
et al.
Abstract: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. Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets. The model features a fo… Show more
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