The recent developments in the field of railway and infrastructure monitoring highlight a significant shift towards leveraging advanced technologies for predictive maintenance and safety enhancements. Ground Penetrating Radar (GPR) techniques, combined with machine learning algorithms such as Support Vector Machines (SVM), Fuzzy C-means, and deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are being refined for more accurate subsurface evaluation of railway tracks. These advancements aim to detect structural anomalies and defects with greater precision, thereby preventing potential accidents. Similarly, the integration of deep learning and computer vision in structural health monitoring of concrete infrastructures is revolutionizing the way damages are detected and evaluated. Techniques such as YOLO-v7 instance segmentation and Mask R-CNN are being employed for automated damage detection, offering scalable and efficient solutions for infrastructure maintenance. Furthermore, the introduction of novel models like CrossDiff, a diffusion probabilistic model with a cross-conditional encoder-decoder, showcases the potential for more accurate crack segmentation in industrial concrete surfaces, addressing the limitations of traditional segmentation methods.

Noteworthy papers include:

• A review on the use of GPR technology for railway track monitoring, highlighting the application of deep learning techniques for enhanced defect detection.
• The introduction of ShaftFormer, a transformer model for predicting railway axle vibrations, offering a robust tool for predictive maintenance.
• A study on data-driven detection of damages in concrete structures using YOLO-v7 and Mask R-CNN, demonstrating the superiority of YOLO-v7 for real-time monitoring.
• The proposal of CrossDiff, a novel diffusion-based model for crack segmentation, which outperforms existing methods in handling slender cracks.