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基于GM-ResNet的智能裂检测方法

Xinran Li¹, Xiangyang Xu¹, Xuhui He²

¹School of Rail Transportation, Soochow University, Suzhou 215006, China.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

这项研究引入了GM-ResNet,用于准确检测道路裂,提高道路安全. 该方法增强了特征提取,并解决了数据不平衡,以获得卓越的性能.

科学领域:

土木工程土木工程是指土木工程.
计算机科学计算机科学

背景情况:

道路安全,结构稳定性和耐用性至关重要.
有效的道路裂检测对于基础设施维护至关重要.

研究的目的:

提出一种增强的深度学习方法 (GM-ResNet),用于精确的道路裂检测.
改进特征提取和解决裂数据集中的类不平衡.

主要方法:

利用ResNet-34进行特征提取,并结合了全球注意力机制.
用多层网络取代完全连接层,以实现复杂的数据关系.
实施焦点损失以减轻类不平衡问题.

主要成果:

与现有方法相比,GM-ResNet显示出更高的裂纹检测准确度.
该方法在检测结果中改善了评估指标.
增强的特征表示和概括导致了更精确的结果.

结论:

拟议的GM-ResNet有效地提高了道路裂检测的精度和有效性.
全球注意力和焦点损失的整合显著改善了模型性能.
这种方法验证了该方法对最佳裂纹检测的有效性.

关键词:

这就是GAM GAM.这就是ResNet ResNet.焦点损失是因为焦点损失.有漏水的 ReLU.道路裂检测道路裂检测

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