Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks

Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks

In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure is con...

Fuld beskrivelse

Bibliografiske detaljer
Main Authors:	Roland Lõuk, Andri Riid, René Pihlak, Aleksei Tepljakov
Format:	Article
Sprog:	English
Udgivet:	MDPI AG 2020-08-01
Serier:	Algorithms
Fag:	pavement distress digital image Deep Learning transfer learning Convolutional Neural Network active learning
Online adgang:	https://www.mdpi.com/1999-4893/13/8/198

Lignende værker

Automated Distress Detection, Classification and Measurement for Asphalt Urban Pavements Using YOLO
af: Paulina Gómez-Conti, et al.
Udgivet: (2023-08-01)

Automated Pavement Distress Detection Based on Convolutional Neural Network
af: Jinhe Zhang, et al.
Udgivet: (2024-01-01)

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction
af: Zaiyan Zhang, et al.
Udgivet: (2024-05-01)

Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge
af: Ye Yuan, et al.
Udgivet: (2023-09-01)

Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network
af: Yudong Han, et al.
Udgivet: (2024-12-01)

Automatic Road Pavement Distress Recognition Using Deep Learning Networks from Unmanned Aerial Imagery
af: Farhad Samadzadegan, et al.
Udgivet: (2024-06-01)

Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network
af: Wafae Hammouch, et al.
Udgivet: (2022-11-01)

UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images
af: Haohui Yan, et al.
Udgivet: (2023-12-01)

Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection
af: Fu-Jun Du, et al.
Udgivet: (2022-05-01)

Automatic Defect Detection of Pavement Diseases
af: Langyue Zhao, et al.
Udgivet: (2022-09-01)

ISTD-PDS7: A Benchmark Dataset for Multi-Type Pavement Distress Segmentation from CCD Images in Complex Scenarios
af: Weidong Song, et al.
Udgivet: (2023-03-01)

Research and optimization of YOLO-based method for automatic pavement defect detection
af: Hui Yao, et al.
Udgivet: (2024-02-01)

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder
af: Rytis Augustauskas, et al.
Udgivet: (2020-04-01)

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines
af: Marco Guerrieri, et al.
Udgivet: (2024-02-01)

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning
af: Sheng Zhang, et al.
Udgivet: (2024-10-01)

PaveDistress: A comprehensive dataset of pavement distresses detectionMendeley Data
af: Zhen Liu, et al.
Udgivet: (2024-12-01)

Common Defects and Distresses in Flexible Pavements in the Middle part of Iraq and Their Main Reasons
af: Khawla H. hassan
Udgivet: (2013-12-01)

Predicting pavement condition index based on the utilization of machine learning techniques: A case study
af: Abdualmtalab Abdualaziz Ali, et al.
Udgivet: (2023-09-01)

Bridging Data Distribution Gaps: Test-Time Adaptation for Enhancing Cross-Scenario Pavement Distress Detection
af: Yushuo Hou, et al.
Udgivet: (2024-12-01)

Multiple distresses detection for asphalt pavement using improved You Only Look Once algorithm based on convolutional neural network
af: Dan, Han-Cheng, et al.
Udgivet: (2024)

PERFORMANCE OF CONCRETE PAVEMENTS AFFECTED BY SUBSURFACE DRAINAGE
af: E. S. Karthic, et al.
Udgivet: (2025-03-01)

Recognition of the Typical Distress in Concrete Pavement Based on GPR and 1D-CNN
af: Juncai Xu, et al.
Udgivet: (2021-06-01)

Application of Mechanistic Empirical Pavement Design Guide Software in Saudi Arabia
af: Abdulrahman Fahad Al Fuhaid, et al.
Udgivet: (2022-08-01)

Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks
af: Danyu Wang, et al.
Udgivet: (2022-08-01)

The Road of Experimental Pavement Structures: Experience of Five Years Operation
af: Audrius Vaitkus, et al.
Udgivet: (2012-09-01)

Polyurethane-Asphaltic Concrete as an Alternative Solution for Road Pavement Distress – A Review
af: Diana Che Lat, et al.
Udgivet: (2024-06-01)

Leveraging Maintenance Management Techniques to Evaluate Pavement Condition Index: Central Iraq Highway System
af: Ahmed Aljubory, et al.
Udgivet: (2025-03-01)

Pavement Distress Estimation via Signal on Graph Processing
af: Salvatore Bruno, et al.
Udgivet: (2022-11-01)

Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
af: Guo-Hong Chen, et al.
Udgivet: (2022-03-01)

A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method
af: Yihan Chen, et al.
Udgivet: (2022-04-01)

Intelligent pavement condition survey: Overview of current researches and practices
af: Allen A. Zhang, et al.
Udgivet: (2024-09-01)

Condition indices for rigid pavements: A comparative analysis of state DOTs using Michigan PMS data
af: Rahul Raj Singh, et al.
Udgivet: (2024-09-01)

Predicting Pavement Condition Index Using Fuzzy Logic Technique
af: Abdualmtalab Ali, et al.
Udgivet: (2022-07-01)

Design Solutions for Pavements Structure Affected by Static and Impact Load
af: Audrius Vaitkus, et al.
Udgivet: (2014-12-01)

DETECTION OF CRACKS IN ASPHALT PAVEMENT DURING ROAD INSPECTION PROCESSES
af: Marcin STANIEK
Udgivet: (2017-09-01)

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions
af: Cheng Chen, et al.
Udgivet: (2021-12-01)

Utility-Based Road Maintenance Prioritization Method Using Pavement Overall Condition Rating
af: Saleh Abu Dabous, et al.
Udgivet: (2020-03-01)

Relationships Between Common Distresses in Flexible Pavements and Physical Properties of Construction Materials Using an Ordinal Logistic Regression Model
af: Uneb Gazder, et al.
Udgivet: (2025-01-01)

Comparison of Pavement Performance Models for Urban Road Management System
af: Igoris Kravcovas, et al.
Udgivet: (2020-08-01)

Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images
af: Carlos Sanchez, et al.
Udgivet: (2025-02-01)