Experimental environment.

<div><p>To improve the accuracy and efficiency of crack segmentation in ancient wooden structures, we propose a lightweight deep neural network architecture, termed SMG-Net. The core innovation of this model lies in its multi-cooperative perception mechanism. First, the proposed Structur...

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Bibliographic Details
Main Author:	Tianke Fang (22648035) (author)
Other Authors:	Zhenxing Hui (22648038) (author), Zhiying Xie (6339833) (author), Peng Yu (48605) (author), Yi Gao (112914) (author), Songdi Shi (22648041) (author), Yuanrong He (22648044) (author)
Published:	2025
Subjects:	Biochemistry Ecology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified statistical significance testing range feature dependencies made publicly available limited computational resources lightweight modular architecture heritage monitoring scenarios global structural consistency cooperative perception mechanism ancient wooden structures ancient wooden components 99 %, respectively div >< p grained crack segmentation fine crack details crack segmentation thereby improving source code shallow features scale features results confirmed proposed structure promote reproducibility particularly suitable multiple orientations module enhances inference speed future research extensive experiments establishes long discriminative capability directional pooling core innovation constructed dataset complex directions coherent recognition blurred edges aware cross 91 %,
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Experimental environment.

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