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CAC:对弱监督裂细分的自信意识辅导培训
Fengjiao Liang1, Qingyong Li1, Xiaobao Li2
1Key Laboratory of Big Data Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education, Beijing 100044, China.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
这项研究引入了一种信任意识的共同培训 (CAC) 框架,通过完善杂的伪标签来改善结构健康监测,以改善弱监督的裂细分 (WSCS).
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:
- 自动裂细分对于结构健康监测至关重要.
- 完全监督的方法需要昂贵的像素级注释.
- 弱监督的裂细分 (WSCS) 面临着杂的伪标签的挑战.
研究的目的:
- 为WSCS.提出一个新的信任意识联合培训 (CAC) 框架.
- 为了解决噪音严重的伪标签问题,并提高细分模型的稳定性.
- 为了提高裂纹检测,代地改进伪标签.
主要方法:
- 一个与两个协作网络共同训练的机制,以学习不确定的裂像素.
- 基于破解信心评分的伪标签的动态分区策略.
- 使用高可信度伪标签用于参数初始化和低可信度标签用于样本多样性.
主要成果:
- 拟议的CAC框架显著优于现有的WSCS方法.
- 在Crack500,DeepCrack和CFD数据集上证明了有效性.
- 通过精致的伪标签实现了更强大的裂细分.
结论:
- 通过管理杂的伪标签,CAC框架为WSCS提供了有效的解决方案.
- 这种方法提高了结构裂检测的可靠性和效率.
- CAC有助于开发更强大的基础设施维护细分模型.
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