Railway track transitions are prone to uneven settlements and track geometry degradation. Traditional monitoring methods are limited in coverage, which highlights the need for novel solutions. This study proposes a method that systematically integrates the high spatial resolution of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) with the broader coverage of Small Baseline

Recent advances in deep learning have enabled automated ground‐penetrating radar (GPR) image analysis, particularly through two‐stage models like mask region‐based convolutional neural (Mask R‐CNN) and single‐stage models like you only look once (YOLO), which are two mainstream approaches for object detection and segmentation tasks. Despite their potential, the limited comparative analysis of these

Near-fault pulse-like ground motions can cause severe damage to long-period engineering structures. A rapid and accurate identification method is essential for seismic design. Deep learning offers a solution by framing pulse-like motion identification as an image classification task. However, the application of deep learning models faces multiple challenges from data and models for pulse-like motion

Excavator safety and efficiency are crucial for construction progress. Monitoring their 3D poses is vital but often hampered by resource and accuracy issues with traditional methods. This paper presents a method to reconstruct the 3D poses of excavators using a cost‐effective monocular camera while considering physical constraints. The approach involves two steps: deep learning to identify 2D key points

Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one‐dimensional

Ground‐penetrating radar (GPR) offers nondestructive subsurface imaging but suffers from a trade‐off between frequency and penetration depth: High frequencies yield better resolution with limited depth, while low frequencies penetrate deeper with reduced detail. This paper introduces a novel frequency enhancement method for GPR images using domain‐adaptive generative adversarial networks. The proposed

Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference

While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic

Although estimating the International Roughness Index (IRI) is crucial, previous studies have faced challenges in addressing IRI prediction for asphalt concrete (AC) overlays on Portland cement concrete (PCC) pavements. This study introduces machine learning to predict the IRI of AC overlays on PCC pavements, focusing on incorporating pre‐overlay treatments to reflect their composite characteristics

The Industrial Internet of Things (IIoT) has emerged as a potent catalyst for transformation across many industries as a part of Industry 4.0. This review thoroughly examines IIoT applications, demonstrating how it enhances operational efficiency, informed decision-making, cost optimization, innovation, and workplace safety. While prior research has often concentrated on technical dimensions such as

The development of privacy-preserving data exchange protocols through Privacy Set Intersection (PSI) protocols has emerged as a critical enabler for secure information exchange in the Internet of Medical Things (IoMT), particularly for applications requiring coordinated data analysis across distributed healthcare systems. Current PSI implementations face two fundamental limitations: a lack of efficient

Vision‐based monitoring of construction equipment is limited in scalability due to the high resource demands of collecting and labeling large datasets across diverse environments. This study proposes a framework that employs Zero‐Shot Learning (ZSL) and Multimodal Large Language Model (MLLM) to recognize construction equipment tasks from video frames without additional training data. The framework

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In image enhancement for low-illumination images, deep learning methods based on the Retinex theory typically decompose the image into illumination and reflectance, followed by iterative optimization or the use of prior custom enhancements. The reflectance map is then approximated as the enhanced image by dividing the radiance by the illumination map. However, this approach does not account for the

Dynamic resolution network is proved to be crucial in reducing computational redundancy by automatically assigning satisfactory resolution for each input image. However, it is observed that resolution choices are often collapsed, where prior works tend to assign images to the resolution routes whose computational cost is close to the required FLOPs. In this paper, we propose a novel optimal transport

Compared with flatbed scanners, portable smartphones provide more convenience for physical document digitization. However, such digitized documents are often distorted due to uncontrolled physical deformations, camera positions, and illumination variations. To this end, we present DocScanner, a novel framework for document image rectification. Different from existing solutions, DocScanner addresses

Despite their impressive performance on various tasks, vision transformers (ViTs) are heavy for mobile vision applications. Recent works have proposed combining the strengths of ViTs and convolutional neural networks (CNNs) to build lightweight networks. Still, these approaches rely on hand-designed architectures with a pre-determined number of parameters. In this work, we address the challenge of

Attention operator has been widely used as a basic brick in visual understanding since it provides some flexibility through its adjustable kernels. However, this operator suffers from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose

The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety and structural stability. While computer vision and deep learning have been widely applied to concrete cracks, domain shift issues often result in the poor performance of pretrained models at new sites. To address this, a self‐supervised domain adaptation method using generative

The assembly and production of sandwich panels for prefabricated components is crucial for the safety of modular construction. Although computer vision has been widely applied in production quality and safety monitoring, the large‐scale differences among components and numerous background interference factors in sandwich panel prefabricated components pose substantial challenges. Therefore, maintaining

In the manufacturing industry, the demand for fault-prediction solutions is increasing to prevent unexpected downtimes and reduce maintenance costs. Although deep-learning methods have demonstrated excellent performance in this domain, the current methods typically overlook the analysis of variable and random processes within mixed-model production, which is a manufacturing strategy that offers flexibility

With the growing demand for oriented object detection (OOD), recent studies on point-supervised OOD have attracted significant interest. In this paper, we propose PointOBB-v3, a stronger single point-supervised OOD framework. Compared to existing methods, it generates pseudo rotated boxes without additional priors and incorporates support for the end-to-end paradigm. PointOBB-v3 functions by integrating

The under-display camera (UDC) technology furnishes users with an uninterrupted full-screen viewing experience, eliminating the need for notches or punch holes. However, the translucent properties of the display lead to substantial degradation in UDC images. This work addresses the challenge of restoring UDC images by specifically targeting the scattering effect induced by the display. We explicitly

Pre-trained vision-language models like CLIP have shown remarkable capabilities across various downstream tasks with well-tuned prompts. Advanced methods tune prompts by optimizing context while keeping the class name fixed, implicitly assuming that the class names in prompts are accurate and not missing. However, this assumption may be violated in numerous real-world scenarios, leading to potential

Insights into the learned latent representations are imperative for verifying deep neural networks (DNNs) in critical computer vision (CV) tasks. Therefore, state-of-the-art supervised Concept-based eXplainable Artificial Intelligence (C-XAI) methods associate user-defined concepts like “car” each with a single vector in the DNN latent space (concept embedding vector). In the case of concept segmentation

Learning robust and scalable visual representations from massive multi-view video data remains a challenge in computer vision and autonomous driving. Existing pre-training methods either rely on expensive supervised learning with 3D annotations, limiting the scalability, or focus on single-frame or monocular inputs, neglecting the temporal information, which is fundamental for the ultimate application

Unmanned aerial vehicles (UAVs) and autonomous ground vehicles are increasingly outfitted with advanced sensors such as LiDAR, cameras, and GPS, enabling real-time object detection, tracking, localization, and navigation. These platforms generate high-volume sensory data, such as video streams and point clouds, that require efficient processing to support timely and informed decision-making. Although

As rotating machinery often operates under complex and variable harsh conditions, domain generalization-based fault diagnosis has been adopted to tackle the challenge of distribution shifts and unseen data in target domains. However, most existing methods depend on fully labeled data from multiple source domains to learn domain-invariant representations. In practice, collecting comprehensive labeled

User centric smart products prioritize the needs and preferences of users, enhancing their experience and satisfaction. Involving users in the design and assessment of smart products ensures that they meet real-world requirements, leading to more intuitive product design, user interface, and functionalities that truly resonate with users. Further, the capability of generating and evaluating many alternative

Depth completion aims to recover dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent depth methods primarily focus on image guided learning frameworks. However, blurry guidance in the image and unclear structure in the depth still impede their performance. To tackle these challenges, we explore a repetitive design in our image guided network to gradually

Directed Energy Deposition (DED) has gained significant interest from the industrial sectors due to its ability to fabricate medium-to-large scale parts with high productivity and low capital investment. Within DED technologies, DED-Arc stands out as a promising method for practical industrial applications. However, the process planning presents challenges for developing an automated system suitable

Classic active contour models (ACMs) are becoming a great promising solution to the contour-based object extraction with the progress of deep learning recently. Inspired by the wave vibration theory in physics, we propose a Generalized Contour Vibration Model (G-CVM) by inheriting the force and motion principle of contour wave for automatically estimating building contours. The contour estimation problems

In fine-grained classification, which is classifying images into subcategories within a common broader category, it is crucial to have precise visual explanations of the classification model’s decision. While commonly used attention- or gradient-based methods deliver either too coarse or too noisy explanations unsuitable for highlighting subtle visual differences reliably, perturbation-based methods

Image matching is a critical task in computer vision research, focusing on aligning two or more images with similar features. Feature detection and description constitute the core of image matching. Handcrafted detectors are capable of obtaining distinctive points but these points may not be repeatable on the image pairs especially those with dramatic appearance changes. On the contrary, the learned