关于标签图像精度对混凝土裂细分网络模型性能影响的研究
Kaifeng Ma1, Mengshu Hao1, Wenlong Shang1
1College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
对于混凝土裂数据集的像素级细标签显著提高了深度学习网络模型的性能. 在裂纹图像细分精度方面,U-Net模型取得了最好的结果.
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
- 人工智能的人工智能
背景情况:
- 高质量的数据集对于深度学习网络模型 (DLNM) 培训和预测准确性至关重要.
- 标签图像的准确性直接影响混凝土裂细分模型的性能.
研究的目的:
- 调查标签图像准确性如何影响混凝土裂细分网络模型的性能.
- 为了比较不同的标签策略来创建语义细分数据集用于裂检测.
主要方法:
- 使用三个策略生成裂纹标签图像:像素级细标签,外部轮扩展和拓结构扩展.
- 构建了三个混凝土裂语义细分数据集,标签准确度各不相同.
- 训练并评估了四种语义细分网络模型 (SSNMs):U-Net,HRNetV2,PSPNet和DeepLabV3+.这些模型包括:
主要成果:
- 来自像素级细标签的数据集在网络模型裂纹图像分割中产生了更高的准确性.
- 在评估的SSNM中,U-Net表现最好,实现了85.47%的平均交叉点 (MIoU),90.86%的平均像素精度 (MPA) 和98.66%的精度.
- U-Net的裂宽度量化显示了与真实裂宽度的最小平均差异 (0.734像素).
结论:
- 像素级精细标签是提高混凝土裂图像细分精度的最佳策略.
- 对于精确的混凝土裂细分任务,U-Net模型是首选选择.
- 准确的数据标签对于基于深度学习的结构性健康监测的可靠性能至关重要.
相关概念视频
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01:28Types of Non-structural Cracks in Concrete
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01:27Shrinkage in Concrete
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