CCMIM:通过状态空间建模和动态特征融合来优化混凝土缺陷检测
1Architectural Engineering Institute, Jinhua University of Vocational Technology, Jinhua, China.
PloS one
|January 27, 2026
概括
本研究介绍了用于准确检测混凝土缺陷的混凝土裂mamba-in-mamba (ccmim) 框架. CCMIM增强了远程依赖性捕获,并降低了计算成本,以改善实时结构健康监测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 混凝土缺陷检测对于结构安全和耐用性至关重要.
- 卷积神经网络 (CNN) 与多层面的特征和全球背景作斗争.
- 变压器模型是计算密集型的,限制了实时应用.
研究的目的:
- 提出一个新的端到端框架,即混凝土裂mamba-in-mamba (CCMIM),用于高效和准确的混凝土缺陷检测.
- 增强模型捕捉远程依赖和全球背景的能力.
- 为了提高强度,适应性,并降低实时检测的计算成本.
主要方法:
- 引入了Mamba-In-Mamba (MiM) 模块,用于层次数据流和远程依赖性捕获.
- 提出了动态双聚变 (DDF) 模块,用于强大的多尺度特征聚变.
- 开发了Sparse Pyramid Transformer (SPT) 模块,以减少计算和保持空间信息,同时提高推断速度.
主要成果:
- 与传统,YOLO和基于变压器的方法相比,CCMIM模型在混凝土裂检测方面表现优越.
- 在多个数据集上实现了高准确率:在RDD2022上达到89.2%,在SDNET2018上达到85.2%,在CCCD上达到79.3%.
- 获得了优秀的mAP50评分:在RDD2022上获得了88.1%,在SDNET2018上获得了87.8%,在CCCD上获得了79.2%.
结论:
- CCMIM框架为具体的缺陷检测提供了有效的解决方案,优于现有的方法.
- 拟议的MIM,DDF和SPT模块有助于提高准确性,稳定性和效率.
- CCMIM为实时结构健康监测和缺陷分析提供了一个有希望的方法.
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