基于深度学习的具体缺陷分类和检测使用语义细分的语义细分
Palisa Arafin1, Ahm Muntasir Billah2, Anas Issa3
1Department of Civil Engineering, Lakehead University, Thunder Bay, ON, Canada.
概括
本研究引入了一个新的数据集和深度学习模型,用于在结构健康监测中检测混凝土裂和裂纹. 基于EfficientNetB3的U-Net在裂纹细分方面获得了95.66%的F1分数.
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:
- 深度学习 (DL) 在结构健康监测 (SHM) 中提供了准确,客观的基础设施损害检测的潜力.
- 主要挑战包括有限的缺陷图像数据集和为实时应用选择合适的DL网络架构.
研究的目的:
- 开发和评估用于使用新型数据集检测混凝土裂和裂纹的DL模型.
- 为了解决现有的缺陷图像数据库和DL网络深度选择的局限性.
主要方法:
- 创建了4087个混凝土裂和1100个裂变图像的多样化数据集.
- 卷积神经网络 (CNN) 分类器 (VGG19,ResNet50,InceptionV3) 用于缺陷的识别.
- 编码-解码模型 (U-Net,PSPNet) 与各种骨干 (VGG19,ResNet50,InceptionV3,EfficientNetB3) 已被开发用于语义细分.
主要成果:
- InceptionV3通过RMSprop优化器实现了91.98%的缺陷分类准确度.
- 基于EfficientNetB3的U-Net产生了最好的裂细分 (95.66% F1得分).
- 基于InceptionV3的U-Net在分割细分方面表现出色 (89.43%的F1得分).
结论:
- 开发的DL模型和数据集在SHM中显著提升了自动视觉损伤检测.
- 特定的CNN架构在混凝土缺陷的分类和细分方面都表现出高效.
- 这项研究为更准确和更容易获得的实时结构性健康监测系统提供了基础.
相关概念视频
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Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
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