SMG-Net:一种轻量级的模块化架构,用于古代木结构中的细粒度裂细分
Tianke Fang1,2, Zhenxing Hui1,2,3, Zhiying Xie1,2
1School of Computing and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, China.
PloS one
|November 19, 2025
概括
一个新的轻量级深度神经网络,SMG-Net,增强了古老木结构中的裂细分. 它实现了高准确性和效率,优于现有的遗产监测方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遗产科学科学 遗产科学
背景情况:
- 准确的裂细分对于评估古代木制遗产的结构完整性至关重要.
- 现有的方法经常与复杂的裂纹图案,噪音和模糊的边缘作斗争,限制了它们的效率.
研究的目的:
- 开发一个轻量级的深度神经网络 (SMG-Net),以提高裂细分的准确性和效率.
- 为了应对识别复杂裂方向的挑战,并加强特征歧视.
主要方法:
- 提出了一个新的SMG-Net架构,具有多合作感知机制.
- 嵌入式结构意识的跨方向聚合 (SACP) 对于长距离的特征依赖.
- 利用多路径稳健特征提取 (MRFE) 和引导语义空间融合 (GSSFusion) 来增强特征集成.
主要成果:
- 在定制和公共数据集上,SMG-Net实现了高 mIoU (81.12%,87.91%) 和PA (98.91%,98.99%).
- 显著超过U-Net,SegFormer和Swin-UNet的表现,通过统计测试证实了这一点.
- 证明了优越的参数效率和推断速度.
结论:
- 在遗产结构中,SMG-Net为裂细分提供了高度准确和高效的解决方案.
- 它的轻量级设计和性能使它成为资源有限的遗产监测的理想选择.
- 该研究提供了公开可用的代码和数据集,以促进未来的研究.
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