The recent advancements in the field of structural health monitoring (SHM) and computer vision have seen a significant shift towards leveraging advanced computational techniques and innovative hardware solutions. The integration of Unmanned Aerial Vehicles (UAVs) with high-resolution photogrammetry has enabled more precise and cost-effective monitoring of infrastructure, particularly in the detection of geometric deformations in bridges. This approach not only reduces the risk associated with traditional inspection methods but also provides comprehensive area-wide quantification of deformation, surpassing classical point or profile measurements.
In the realm of machine learning, Vision Transformers (ViTs) have been upsampled and utilized in unsupervised and weakly supervised tasks, demonstrating strong performance in object localization, segmentation, and materials characterization. These features, extracted from ViT networks like DINOv2, have shown to capture complex relationships that are inaccessible to traditional methods, thereby enhancing the speed and accuracy of downstream tasks.
Another notable development is the introduction of Mamba-based models, which offer a computationally efficient alternative to traditional Convolutional Neural Networks (CNNs) and Vision Transformers. Models like CrackMamba and SepMamba have achieved state-of-the-art performance in crack segmentation and speaker separation, respectively, while significantly reducing computational costs and memory usage. These models leverage the strengths of Mamba's linear computational complexity and bidirectional processing capabilities, making them highly suitable for real-world applications.
Furthermore, the field is witnessing a move towards hybrid systems that combine multiple data sources and human-in-the-loop approaches to address the limitations of image-based SHM, particularly in mitigating false positives and negatives. These strategies aim to improve the reliability and practical applicability of SHM techniques in real-world infrastructure monitoring.
In summary, the current direction of the field is characterized by the adoption of advanced computational models, innovative hardware solutions, and hybrid approaches that aim to enhance the precision, efficiency, and reliability of structural health monitoring and computer vision tasks.
