Results 11 to 20 of about 9,836 (188)

Transformer–CNN Hybrid Framework for Pavement Pothole Segmentation [PDF]

Sensors
Pavement surface defects such as potholes pose significant safety risks and accelerate infrastructure deterioration. Accurate and automated detection of such defects requires both advanced sensing technologies and robust deep learning models.
Tianjie Zhang, Zhen Liu, Bingyan Cui, Xingyu Gu, Yang Lu   +4 more
doaj   +2 more sources

Digitalization and Automation of Runway Inspection Using Unmanned Aerial Vehicles [PDF]

Sensors
This paper presents an end-to-end framework for automated inspection and condition assessment of airport runway pavement using UAV-acquired imagery. The proposed approach integrates Unmanned Aerial Vehicle (UAV)-based data collection, deep learning-based
Marios Krestenitis, Alexandros Petropoulos, Ilias Koulalis, Irina Stipanovic, Sandra Skaric Palic, Konstantinos Ioannidis, Stefanos Vrochidis   +6 more
doaj   +2 more sources

PaveDistress: A comprehensive dataset of pavement distresses detectionMendeley Data [PDF]

Data in Brief
The PaveDistress dataset contains high-resolution images of road surface distresses, including cracks, repairs, potholes, and background images without defects.
Zhen Liu, Wenxiu Wu, Xingyu Gu, Bingyan Cui   +3 more
doaj   +2 more sources

Automated Detection and Mapping of Pavement Cracks from Videos for Road Inspections [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Road inspections are essential for assessing pavement conditions and planning maintenance to extend infrastructure lifespan. Cracks are key indicators of distress, and their timely detection can reduce repair costs.
G. Perda, M. O. I. Eid, N. Padkan, N. Padkan, S. Malek, L. Morelli, F. Remondino   +6 more
doaj   +1 more source

Automated Bridgehead Settlement Detection on the Non-Staggered-Step Structures Based on Settlement Point Ratio Model

Applied Sciences, 2023
The bridgehead settlement problem continues to be one of the most chronic issues affecting long-term bridge performance. In addition, the magnitude of non-staggered-step settlement across the bridge approach transition has not been quantified.
Hong Lang, Yuan Peng, Zheng Zou, Shengxue Zhu, Zhen Chen, Meng Zhang   +5 more
doaj   +1 more source

Railway track condition assessment at network level by frequency domain analysis of GPR data [PDF]

, 2018
The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly ...
Fontul, Simona, Paixão, André, Pajewski, Lara, Solla, Mercedes   +3 more
core   +2 more sources

A Framework for Automated Pavement Condition Monitoring [PDF]

, 2016
Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task.
,, Brilakis, I, Doycheva, K, Koch, C, Radopoulou, SC   +4 more
core   +2 more sources

A New Method for Extracting Typical Diseases of Highway Asphalt Pavement in High-Resolution Images Based on YOLO Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The exponential expansion of global highway infrastructure has intensified challenges in road inspection and maintenance operations, creating substantial demand for advanced automated interpretation systems.
Xinxiu Zhang, Qing Zhu, Zhihua Zhang, Shuwen Yang, Hui Xu, Fukui Zhang, Li Hou, Jinhao Cai, Yiqian Ma, Lujia Zhao   +9 more
doaj   +1 more source

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

, 2019
Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive.
Bocus, Mohammud Junaid, Cheng, Shanshan, Fan, Rui, Jiao, Jianhao, Liu, Ming, Ma, Fulong, Wang, Li, Zhu, Yilong   +7 more
core   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source