薛亚东1,2?覮,高健1,2,李宜城3,黄宏伟1,2.基于深度学习的地铁隧道衬砌病害检测模型优化[J].湖南大学学报:自然科学版,2020,(7):137~146
基于深度学习的地铁隧道衬砌病害检测模型优化
Optimization of Shield Tunnel Lining Defect Detection Model Based on Deep Learning
  
DOI:
中文关键词:  地铁盾构隧道  裂缝  渗漏水  深度学习  病害检测
英文关键词:subway shield tunnel  crack  leakage  deep learning  defect detection
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作者单位
薛亚东1,2?覮,高健1,2,李宜城3,黄宏伟1,2 (1. 同济大学 地下建筑与工程系上海200092 2. 同济大学 岩土及地下工程教育部重点实验室上海200092 3. 中建丝路建设投资有限公司陕西 西安 710000) 
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中文摘要:
      地铁盾构隧道衬砌病害检测面临的最主要问题是如何获取高质量的病害图片以及如何快速、准确实现病害检测. 基于CCD线阵相机设计制造了地铁隧道病害检测车,并针对上海运营地铁1、2、4、7、8、10、12等线路采集了大量的衬砌图像,通过手工标注建立高质量隧道病害样本库. 基于卷积神经网络Faster R-CNN(Faster Region-based Convolutional Neural Network),构建了病害自动检测深度学习框架. 考虑到裂缝及渗漏水病害的特殊性,采用数据统计分析及K-means聚类算法分析其几何特征,结合病害特征优化VGG-16网络模型中的anchor box相关参数. 结果表明,修正后的模型病害检测准确度有明显的提升(约7%),同时模型的训练时间减少. 经验证,上述方法同样可提高裂缝或渗漏水单一病害识别模型的准确度.
英文摘要:
      The main problems in the detection of shield tunnel lining defects are how to obtain high quality images of different defects and how to quickly and accurately detect the defects. A device for mobile tunnel inspection (MTI-100) was designed and manufactured based on CCD line array cameras. Using MTI-100, Shanghai Metro Lines 1, 2, 4, 7, 8, 10 and 12 were tested and a large number of lining images were obtained. These images were manually labeled to form a high quality database of lining defects samples. Based on the Faster R-CNN (Faster Region-based Convolutional Neural Network), a deep learning framework for automatic disease detection was established. Inspired by the existing model VGG16, the CNN detection model of tunnel lining defects was established. Considering the particularity of cracks and leakage defects, statistical analysis and K-means clustering algorithm were used to analyze the geometric features, so as to optimize the related parameters of anchor box in the VGG-16 network model. The results show that the accuracy of the optimizeation is greatly improved(about 7%),and the training time is reduced. It is verified that the method can also improve the accuracy of the defect detection model of crack or leakage singly.
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