论文标题
缺陷分割：使用卷积神经网络与地面穿透雷达数据的内部缺陷映射隧道内线
Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
论文作者
论文摘要
这项研究提出了一种地面穿透性雷达（GPR）数据处理方法，用于对内部缺陷的无损检测，称为缺陷分段。为了执行自动隧道衬里检测的关键步骤，该方法使用了一个称为SEGNET的CNN与LovászSoftMax损耗函数结合使用GPR合成数据，以绘制内部缺陷结构，从而提高了缺陷的准确性，自动化和效率。我们提出的新方法克服了对合成数据和真实数据的评估证明的传统GPR数据解释的几个困难 - 为了验证实际数据上的方法，设计和构建了包含已知缺陷的测试模型，并获得并分析了GPR数据。
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 Lovász 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.
