Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

• URL: http://arxiv.org/abs/2003.13120v1
• Date: Sun, 29 Mar 2020 19:30:59 GMT
• Title: Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
• Authors: Senlin Yang, Zhengfang Wang, Jing Wang, Anthony G. Cohn, Jiaqi Zhang, Peng Jiang, Peng Jiang, Qingmei Sui
• Abstract summary: This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects. The method uses a CNN called Segnet combined with the Lov'asz softmax loss function to map the internal defect structure with GPR synthetic data.
• Score: 13.469645178974638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation. To perform this critical step of automatic tunnel lining detection, the method uses a CNN called Segnet combined with the Lov\'asz softmax loss function to map the internal defect structure with GPR synthetic data, which improves the accuracy, automation and efficiency of defects detection. The novel method we present overcomes several difficulties of traditional GPR data interpretation as demonstrated by an evaluation on both synthetic and real datas -- to verify the method on real data, a test model containing a known defect was designed and built and GPR data was obtained and analyzed.

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