Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
This work addresses the need for automated and efficient defect detection in tunnel infrastructure, offering a novel approach that overcomes traditional GPR interpretation difficulties, though it is incremental as it adapts existing CNN methods to a specific domain.
The research tackled the problem of non-destructive detection of internal defects in tunnel linings by proposing a defect segmentation method using Ground Penetrating Radar (GPR) data with a convolutional neural network (Segnet) and Lovász softmax loss, which improved accuracy, automation, and efficiency in defect detection as validated on synthetic and real data.
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.