MDEM:一个多层次的损坏增强MambaOut铺路损坏分类
Shizheng Zhang1, Kunpeng Wang1, Pu Li1
1Software Engineering College, Zhengzhou University of Light Industry, 136 Science Avenue, Zhengzhou 450000, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
一种新的路面损坏分类模型,多尺度损坏增强MambaOut (MDEM),通过准确识别各种损坏类型,甚至在背景噪音下,改善道路维护.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路工程工程的道路工程.
背景情况:
- 准确的路面损坏分类对于道路维护和驾驶安全至关重要.
- 传统方法面临的挑战是由于不同程度的损伤,不规则的形状,小面积比率和背景噪音.
研究的目的:
- 开发一种新的路面损坏分类模型,多尺度损害增强MambaOut (MDEM),以克服现有方法的局限性.
- 提高在复杂的现实条件下对路面损坏识别的准确性和稳定性.
主要方法:
- 引入了多级损害增强MambaOut (MDEM) 模型,包括多级动态特征融合块 (MDFF) 和损害细节增强块 (DDE).
- 多年来,MDFF通过适应性整合了多层次信息,以加强特征提取,区分不同层次的裂.
- DDE强调精细的结构细节,并抑制背景噪声,以改善小规模损坏的表现.
主要成果:
- 在多个数据集 (CQU-BPMDD,CQU-BPDD,Crack500-PDD) 上,MDEM实现了卓越的性能.
- 在CQU-BPMDD数据集中,MDEM比基线提高了2.01%的准确性,2.64%的精度,2.7%的F1得分和4.2%的AUC.
- 在路面损坏分类方面,MDEM显著优于MambaOut和其他可比方法.
结论:
- MDEM模型有效地解决了路面损坏分类的挑战,包括尺度变化,不规则的形状,小面积和背景噪音.
- 对于现实世界的道路维护应用,MDEM提供了更高的检查准确性.
- 拟议的模型代表了自动化路面状况评估的重大进步.
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