{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T09:51:01Z","timestamp":1778233861319,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Xinjiang Uygur Autonomous Region","award":["2020A03003-5"],"award-info":[{"award-number":["2020A03003-5"]}]},{"name":"Science and Technology Major Project of Xinjiang Uygur Autonomous Region","award":["2023M730204"],"award-info":[{"award-number":["2023M730204"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020A03003-5"],"award-info":[{"award-number":["2020A03003-5"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M730204"],"award-info":[{"award-number":["2023M730204"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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 four-tier sparse encoder for down-sampling and a two-tier sparse decoder for up-sampling, connected via a conventional convolutional neck, forming a semi-symmetrical structure. This design enhances the model\u2019s ability to capture essential low-level features, including geometric shapes and object boundaries. Additionally, to circumvent the trivial solutions in pixel regression that the original masked autoencoder faced, Histogram of Oriented Gradients (HOG) descriptors and Laplacian features have been integrated as novel self-supervision targets. Testing shows that the proposed model can effectively discern essential features of muck images in self-supervised training. When applied to subsequent end-to-end training tasks, it enhances the model\u2019s performance, increasing the prediction accuracy of Intersection over Union (IoU) for muck boundaries and regions by 5.9% and 2.4%, respectively, outperforming the enhancements made by the original masked autoencoder.<\/jats:p>","DOI":"10.3390\/sym16020222","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T08:16:52Z","timestamp":1707725812000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation"],"prefix":"10.3390","volume":"16","author":[{"given":"Ke","family":"Lei","sequence":"first","affiliation":[{"name":"Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongsheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuying","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103097","DOI":"10.1016\/j.tust.2019.103097","article-title":"Application and Outlook of Information and Intelligence Technology for Safe and Efficient TBM Construction","volume":"93","author":"Li","year":"2019","journal-title":"Tunn. 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