Infrastructure Maintenance and Monitoring

Últimos artículos

• An End-to-End Automated Pipeline for Controllable Crack Data Synthesis

  Conghui Li, Muxin Pu, Chern Hong Lim, Weiyao Lin, Xin Wang · 14 de septiembre de 2026

  Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing generative augmentation methods often treat crack synthesis as a generic image-generation task, offering insufficient control over morphology, bound…

• TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aerial Systems Inspection of Flat Roofs

  Samuel Dunthorne, Hashim A. Hashim · 14 de septiembre de 2026

  Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereby contributing directly to household energy consumption, carbon emissions, and long-term environmental sustainability. Timely detection of roof defec…

• Vision Transformer-Based Multi-Level Feature Fusion for Multi-Label Sewer Defect Classification

  Xu Fang, Zhuoran Wang, Qing Li, Shengyu Zhang, Guanzhi Deng, Jianbiao He, Qingquan Li · 11 de septiembre de 2026

  Automated classification of sewer defects is essential for infrastructure condition assessment and maintenance decision-making, but existing deep learning methods struggle to balance classification accuracy and computational complexity in large-scale multi-label scenarios. This study develops Sewer-…

• Infra-Bench CLS: A Global, Open-Source Benchmark for Critical Infrastructure Classification with Earth Observation Foundation Models

  Justin Guthrie, Edward Oughton, Konrad Wessels, Matthew Rice, Isaac Corley · 10 de septiembre de 2026

  Critical infrastructure location data is often incomplete and unevenly distributed globally, especially in developing regions. Earth observation foundation models are proposed as a new step in enabling us to more efficiently understand the natural and built environment, raising questions as to their…

• Depth-Aware Pothole Detection Using YOLO and RT-DETR at the Edge

  Md Monjurul Ahsan Prodhan, Md Nour Hossain · 31 de agosto de 2026

  Pothole detection and its severity measurement is still an important challenges in urban infrastructure management, where late maintenance directly contributes to vehicle damage, road accidents, and escalating repair costs. Existing automated approaches depend on 2D RGB images and cannot measure phy…

• Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection

  Andrii Kompanets, Finn Michael Sherry, Remco Duits, Davide Leonetti, H. H. Snijder · 19 de agosto de 2026

  Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance costs. Much research has been done on automating this process using computer vision methods, which are often evaluated and compared using metrics such …

• YOLO26-RD: An End-to-End Road Damage Detection Network With Learnable Contrast Enhancement and Edge-Guided Downsampling

  Sompote Youwai, Pawarotorn Chaipetch, Hathairat Samaikul, Theerayut Yonseng · 18 de agosto de 2026

  Pavement distress detectors are conventionally specialised for small objects, typically by adding a stride-4 detection head and replacing strided convolution with space-to-depth downsampling. This paper tests that premise against the annotation geometry of region level survey imagery and finds it fa…

• RMR-P: Road Metadata-Aware Restoration for Pavement Inspection

  Amir Ghorbani, Amirali K. Gostar, WeiQin Chuah, Vahid Ghorbani, Aidan Blair, Reza Hoseinnezhad, Alireza Bab-Hadiashar · 11 de agosto de 2026

  Road-surface images captured by vehicle-mounted cameras are often degraded by motion blur, defocus, poor illumination, and noise due to vehicle motion, camera limitations, and varying environmental conditions. These degradations can obscure thin cracks and pothole boundaries that are critical for ac…

• Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalities

  Hui Liu, Chen Jia, Fan Shi, Xu Cheng, Mianzhao Wang, Shengyong Chen · 5 de agosto de 2026

  In multimodal crack segmentation for industrial facilities, the key challenge is preventing missing modalities from degrading pixel-level performance while maintaining low computational cost. Existing methods struggle to address semantic degradation caused by missing modalities. We propose Compass, …

• Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

  Jianglei Xing, Xiao Tan, Dongzhao Jin, Pengwei Guo, Yuhuan Wang, Huiya Niu · 4 de agosto de 2026

  This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was established, and 14 input variables related to asphalt properties, aggregate gradation, a…

• Training-Free Decoding of SAM3 Semantic Responses for Cross-Domain Infrastructure Crack Segmentation

  Zhanping Song, Shipeng Liu, Liang Zhao, Dengfeng Chen · 15 de julio de 2026

  Cross-domain infrastructure crack segmentation is challenged by variations in materials, imaging conditions, crack morphology, and background interference. Although text-promptable foundation models reduce the need for task-specific training, SAM3's native proposal interface may not fully expose the…

• Dynamic Object Detection and Tracking in Construction: A Fisheye Camera and LiDAR Sensor Fusion Model

  Yilong Chen, Huili Huang, Yong K. Cho · 9 de julio de 2026

  Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environments such as construction sites. While LiDAR-based SLAM and occupancy grid methods offer viable solutions for detecting and tracking motion, many state…

• Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization

  Zhiran Yan, Yutong Xin, S Shyam Shenoi, Rui Song, Gordon Elger · 7 de julio de 2026

  Autonomous vehicles often rely on high-definition (HD) maps for navigation; however, these maps are not frequently updated and often lack semi-static information, such as temporary roadwork zones, which can significantly alter the road network. This limitation underscores the urgent need for an accu…

• A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions

  Yamil Uchani, Grace Luna, Edwin Salcedo, Mauricio Figueroa · 7 de julio de 2026

  UAV-based pavement inspection can reduce the cost and risk of road-surface monitoring, but real-world deployment remains difficult when traffic, pedestrians, and temporary occlusions affect defect visibility. This paper presents a Unity-based digital twin framework for traffic-aware UAV pavement mon…

• A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring without Lane Closure

  Yamil Uchani, Grace Abigail Luna Verdueta, Mauricio Figueroa, Edwin Salcedo · 23 de junio de 2026

  UAV-based pavement inspection can reduce the cost and risk of road-surface monitoring, but real-world deployment remains difficult when traffic, pedestrians, and temporary occlusions affect the visibility of defects. This paper presents a Unity-based digital twin framework for traffic-aware UAV pave…

• YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection

  Ching-Yu Tsai, Chia-Min Lin, Chih-Hsiang Yang, Yung-Che Wang, Jen-Shiun Chiang · 12 de junio de 2026

  Crack detection plays an important role in infrastructure inspection and Structural Health Monitoring (SHM). However, cracks typically appear as thin, low-contrast structures and are easily affected by background noise, posing challenges for existing object detection models. This study proposes an i…

• WHU-Infra3D: A Full-stack Multi-modal Dataset and Benchmark for 3D Roadside Infrastructure Inventory

  Chong Liu, Luxuan Fu, Xuyu Feng, Zhen Dong, Bisheng Yang · 10 de junio de 2026

  The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets. However, existing datasets predominantly focus on coarse visual perception, lacking the strict multi-modal alignment and attribute and status diagnosis required…

• Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection

  Mattia Forlesi, Alfonso Esposito, Ivan Zyrianoff, Alessandro Marzani, Marco Di Felice · 9 de junio de 2026

  Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance str…

• Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure

  Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Armstrong Aboah · 5 de junio de 2026

  Reliable crack assessment requires not only accurate pixel-level masks but also connected crack geometry and confidence estimates that remain stable under domain shift. However, existing segmentation models can achieve high overlap scores while fragmenting cracks, missing fine branches, and providin…

• Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study

  Ching Yau Fergus Mok, Lavindra de Silva, Varun Kumar Reja, Ioannis Brilakis · 5 de junio de 2026

  Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data. This work exploits the relational nature of continuous asset condition monitoring to reformulate image-based defect detection as image difference classification (IDC) to…

• Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection

  Yuhu Feng, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 3 de junio de 2026

  The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to stringent privacy and security regulations. While federated learning (FL) offers a privacy-preserving collaborative alternative, its application to natio…

• Training-Free Object-Agnostic Jam Detection in Fulfillment Centers

  Ruiliang Liu, Tina Dongxu Li, Joshua Migdal, Fernando Ruch, Kenneth Meszaros, Moses Trevor Dardik · 2 de junio de 2026

  In fulfillment centers, diverse objects move continuously from inbound to outbound operations and can become jammed due to excessive conveyor friction, incorrect orientation, or mechanical failures. Traditional jam detection approaches rely on object detection models to identify objects, followed by…

• Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling

  Shipeng Liu, Liang Zhao, Dengfeng Chen, Weihua Zhang · 1 de junio de 2026

  Recent crack segmentation methods often follow generic semantic segmentation designs, using stronger backbones, hybrid CNN-Transformer-Mamba encoders, and auxiliary enhancement branches. Although effective, this raises whether stronger generic feature mixing is the most suitable direction for crack …

• Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis

  Marouane Elmegdar, Teng Xiao · 29 de mayo de 2026

  Image-based 3D reconstruction offers a low-cost alternative to traditional sensor-based techniques for road surface assessment. This study compares four reconstruction pipelines--COLMAP, Meshroom, Metashape, and 3D Gaussian Splatting (3DGS)--to evaluate their ability to estimate road surface roughne…

• Pixel-Level Pavement Distress Assessment Using Instance Segmentation

  Logan Dewick, Bibesh Pyakurel, Kong Pheng Yang, Nazim Choudhury, M. G. Sarwar Murshed · 26 de mayo de 2026

  Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necessary for maintenance-relevant quantification. This paper presents a v…