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水工混凝土裂缝像素级形态分割与特征量化方法
引用本文:任秋兵,李明超,沈扬,张野,白硕. 水工混凝土裂缝像素级形态分割与特征量化方法[J]. 水力发电学报, 2021, 40(2): 234-246. DOI: 10.11660/slfdxb.20210224
作者姓名:任秋兵  李明超  沈扬  张野  白硕
摘    要:混凝土开裂问题在水工建筑物主体结构中普遍存在,裂缝检测一直是水工混凝土结构安全鉴定的重要内容.数字图像处理技术因具有效率高、成本低等优势而被广泛应用于结构表面裂缝检测中,形态分割与特征量化是其核心任务.针对传统图像处理人工干预较多、泛化能力较弱等不足,本文提出了一种基于深度卷积神经网络的水工混凝土裂缝像素级形态分割与特...

关 键 词:水工混凝土  裂缝检测  语义分割  特征量化  深度学习

Pixel-level shape segmentation and feature quantification of hydraulic concrete cracks based on digital images
REN Qiubing,LI Mingchao,SHEN Yang,ZHANG Ye,BAI Shuo. Pixel-level shape segmentation and feature quantification of hydraulic concrete cracks based on digital images[J]. Journal of Hydroelectric Engineering, 2021, 40(2): 234-246. DOI: 10.11660/slfdxb.20210224
Authors:REN Qiubing  LI Mingchao  SHEN Yang  ZHANG Ye  BAI Shuo
Abstract:Concrete cracking is common in the main structures of hydraulic buildings, and crack detection is always crucial to hydraulic engineering safety appraisal. Shape segmentation and feature quantification are the main tasks of digital image processing technology that has been widely used in structural surface crack detection due to its advantages of high efficiency and low cost, but traditional image processing has the shortcomings of more manual intervention and weaker generalization ability. This paper describes a new method for pixel-level shape segmentation and feature quantification of hydraulic concrete cracks based on deep convolutional neural networks. This method is on the basis of U-net semantic segmentation architecture and incorporates the transfer learning technology. Specifically, it uses a pre-trained VGG16 network to enhance the encoder and extract multi-scale and high-level semantic information, and alleviates class imbalance problems by improving the cross-entropy loss function, so that the crack shape can be accurately segmented. Then, we present a set of algorithms based on the binarized segmentation mask and computer vision technology for calculating key geometric parameters such as crack area, length and width. To verify and evaluate this crack detection method, we generate an image dataset of hydraulic concrete cracking through numerical simulations and conduct comparative experiments demonstrating its effectiveness and superiority. The results indicate that its segmentation effect is significantly better than that of classical image segmentation methods, and its calculations of crack feature parameters meet the required detection accuracy. Thus, it is a new useful technique for quality control of hydraulic concrete structures.
Keywords:hydraulic concrete  crack detection  semantic segmentation  feature quantification  deep learning  
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