A Fast Detection Method via Region-Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects

作者：Xue, Yadong^*; Li, Yicheng

来源：Computer-Aided Civil and Infrastructure Engineering, 2018, 33(8): 638-654.

DOI：10.1111/mice.12367

• CRACK DETECTION
• IDENTIFICATION
• RECOGNITION

Tunnel lining defects are an important indicator reflecting the safety status of shield tunnels. Inspired by the state-of-the-art deep learning, a method for automatic intelligent classification and detection methodology of tunnel lining defects is presented. A fully convolutional network (FCN) model for classification is proposed. Information about defects, collected using charge-coupled device cameras, was used to train the model. The model's performance was compared to those of GoogLeNet and VGG. The best-set accuracy of the proposed model was over 95% at a test-time speed of 48 ms per image. For defects detection, image features were computed from large-scale images by the FCN and then detected using a region proposal network and position-sensitive region of interest pooling. Some indices (detection rate, detection accuracy, and detection efficiency, locating accuracy) were used to evaluate the model. The comparisons with faster R-CNN and a traditional method were conducted. The results show that the model is very fast and efficient, allowing automatic intelligent classification and detection of tunnel lining defects.

• 出版日期2018-8
• 单位同济大学