Advanced technology in railway track monitoring using the GPR Technique: A Review

Subsurface evaluation of railway tracks is crucial for safe operation, as it allows for the early detection and remediation of potential structural weaknesses or defects that could lead to accidents or derailments. Ground Penetrating Radar (GPR) is an electromagnetic survey technique as advanced non-destructive technology (NDT) that can be used to monitor railway tracks. This technology is well-suited for railway applications due to the sub-layered composition of the track, which includes ties, ballast, sub-ballast, and subgrade regions. It can detect defects such as ballast pockets, fouled ballast, poor drainage, and subgrade settlement. The paper reviews recent works on advanced technology and interpretations of GPR data collected for different layers. Further, this paper demonstrates the current techniques for using synthetic modeling to calibrate real-world GPR data, enhancing accuracy in identifying subsurface features like ballast conditions and structural anomalies and applying various algorithms to refine GPR data analysis. These include Support Vector Machine (SVM) for classifying railway ballast types, Fuzzy C-means, and Generalized Regression Neural Networks for high-accuracy defect classification. Deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are also highlighted for their effectiveness in recognizing patterns associated with defects in GPR images. The article specifically focuses on the development of a Convolutional Recurrent Neural Network (CRNN) model, which combines CNN and RNN architectures for efficient processing of GPR data. This model demonstrates enhanced detection capabilities and faster processing compared to traditional object detection models like Faster R-CNN.

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.