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18 March 2024 Detection of moss coverage and growth rate on rock surfaces based on semantic segmentation
Qingyi Zhai, Huiling Liu, Bo Wan, Quanhua Xie, Xuefeng He, Yang Liu, Lei Wu, Dong Lai, Yuanyuan He, Xiaoling Peng, Yang Liu, Nianbing Zhong
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Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131041T (2024) https://doi.org/10.1117/12.3022678
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China
Abstract
Moss can cause significant damages to stone artifacts. In order to effectively protect stone artifacts, it is crucial to accurately obtain information about the moss growth on the surface of the stone artifacts. In response to the laborious and inefficient process of manually detecting moss coverage on stone artifacts, this paper introduces an enhanced semantic segmentation model that integrates Swin Transformer and convolutional neural networks to accurately detect the moss coverage and growth rate. Through a comparison with current state-of-the-art models, our model outperforms them by the average intersection over union on the LoveDA and moss datasets, reaching a result of 94.79%. on the moss dataset. This indicates that our model can accurately segment rocks and moss, and thus enabling the effective calculation of moss coverage and growth rate on stone artifacts and providing vital support for their preventive protection.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Qingyi Zhai, Huiling Liu, Bo Wan, Quanhua Xie, Xuefeng He, Yang Liu, Lei Wu, Dong Lai, Yuanyuan He, Xiaoling Peng, Yang Liu, and Nianbing Zhong "Detection of moss coverage and growth rate on rock surfaces based on semantic segmentation", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131041T (18 March 2024); https://doi.org/10.1117/12.3022678
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KEYWORDS
Image segmentation
Data modeling
Semantics
Deep learning
Transformers
Education and training
Image enhancement
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