SMG-Net: A lightweight modular architecture for fine-grained crack segmentation in ancient wooden structures
Tianke Fang, Zhenxing Hui, Zhiying Xie, Peng Yu, Yi Gao, Songdi Shi, Yuanrong He
TL;DR
SMG-Net is a lightweight neural network that improves crack segmentation in ancient wooden structures with high accuracy and efficiency.
Contribution
The paper introduces a novel multi-cooperative perception mechanism for crack segmentation in heritage structures.
Findings
SMG-Net achieved mIoU scores of 81.12% and 87.91% on crack datasets.
The model outperformed U-Net, SegFormer, and Swin-UNet in accuracy and speed.
SMG-Net is parameter-efficient and suitable for low-resource heritage monitoring.
Abstract
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 Structure-Aware Cross-directional Pooling (SACP) establishes long-range feature dependencies in multiple orientations, addressing the challenge of coherent recognition for cracks with complex directions. Second, the Multi-path Robust Feature Extraction (MRFE) module enhances the tolerance of the model to noise and blurred edges, thereby improving the discriminative capability of shallow features. Third, the Guided Semantic–Spatial Fusion (GSSFusion) mechanism enables efficient alignment and integration of multi-scale features, ensuring both fine crack details and global structural consistency in segmentation. Extensive…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Masonry and Concrete Structural Analysis · Handwritten Text Recognition Techniques