Results 1 to 10 of about 640 (153)

LTPLN: Automatic pavement distress detection. [PDF]

PLoS ONE
Automatic pavement disease detection aims to address the inefficiency in practical detection. However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and ...
Wen-Qing Huang, Liu Feng, Yuan-Lie He
doaj   +4 more sources

Pavement Distress Detection Methods: A Review [PDF]

Infrastructures, 2018
The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the ...
Antonella Ragnoli, Maria Rosaria De Blasiis, Alessandro Di Benedetto   +2 more
doaj   +6 more sources

Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin

Applied Sciences, 2023
The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance.
Weidong Wang, Xinyue Xu, Jun Peng, Wenbo Hu, Dingze Wu   +4 more
doaj   +3 more sources

LRD-DETR: A Lightweight RT-DETR-Based Model for Road Distress Detection [PDF]

Sensors
Intelligent road distress detection technology has emerged as an important research topic in the field of highway maintenance. However, the accuracy and practicality of pavement distress detection are constrained by multiple factors, primarily including ...
Chen Dong, Yunwei Zhang
doaj   +2 more sources

A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction [PDF]

Scientific Data
In recent years, automated detection technologies for large-scale pavement distress have become a focal point of research in the transportation sector.
Hanlin Yang, Jinpu Cao, Jun Wan, Qian Gao, Chenglong Liu, Martin Fischer, Yuchuan Du, Yishun Li, Pooja Jain, Difei Wu   +9 more
doaj   +2 more sources

Attain: Inclusive annotated pavement distress types and severity datasetMendeley Data [PDF]

Data in Brief
Pavement distress detection plays a crucial role in pavement management and rehabilitation (M&R) by providing essential data for maintenance decision-making.
Mohammad Rezaeimanesh, Amir Golroo, Mohammad Sadegh Fahmani, Mohammad Javad Amani, Farid Hasanitabaar, Mohammad Saleh Entezari, Sane Karimi   +6 more
doaj   +2 more sources

Automated classification and detection of multiple pavement distress images based on deep learning

Journal of Traffic and Transportation Engineering (English ed. Online), 2023
To achieve automatic, fast, efficient and high-precision pavement distress classification and detection, road surface distress image classification and detection models based on deep learning are trained. First, a pavement distress image dataset is built,
Deru Li, Zhongdong Duan, Xiaoyang Hu, Dongchang Zhang, Yiying Zhang   +4 more
doaj   +3 more sources

Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada

International Journal of Transportation Science and Technology, 2022
Pavement distresses, including cracking and disintegration, deteriorate road user’s comfort, damage vehicles, increase evasive maneuvers, and increase emissions.
Ce Zhang, Ehsan Nateghinia, Luis F. Miranda-Moreno, Lijun Sun   +3 more
doaj   +3 more sources

Applying a Combination of the YOLOv8 Model and 3D Point Cloud Images in Asphalt Pavement Maintenance [PDF]

Sensors
Asphalt pavement distress detection plays a pivotal role in highway maintenance, providing an essential basis for optimizing maintenance strategies and allocating funding. Consequently, quick detection and efficient identification of distress are crucial
Yangyang Wang, Shoujing Yan, Weibo Shi, Chenchen Xi, Jiachen Shi, Fengxia Chi, Jintao Wei   +6 more
doaj   +2 more sources

Onboard LiDAR–Camera Deployment Optimization for Pavement Marking Distress Fusion Detection [PDF]

Sensors
Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service.
Ciyun Lin, Wenjian Sun, Ganghao Sun, Bown Gong, Hongchao Liu   +4 more
doaj   +2 more sources