research-article

Pavement Crack Detection using Convolutional Neural Network

Authors:

Nhung Thi Hong Nguyen,

Thi Thuy NguyenAuthors Info & Claims

SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

Pages 251 - 256

https://doi.org/10.1145/3287921.3287949

Published: 06 December 2018 Publication History

Abstract

Pavement crack detection is an important problem in road maintenance. There are many processing methods, including traditional and modern methods, solving this issue. Traditional methods use edge detection or some other digital image processing for crack detection, but these approaches are sensitive to many types of noise and unwanted objects on the road. For the purpose of increasing accuracy, image pre-processing methods are required for many of these techniques. Recently, some techniques that utilize deep learning to detect cracks in images have achieved high accuracy, without pre-processing. However, some of them are very complicated, some make use of manually collected data and some methods still need some form of pre-processing. In this paper, we propose a method that applies a convolutional neural networks to detect cracks in pavement images. Our research uses two data sets, one public data set and the other collected by ourselves. We also experimentally compare our method with some exiting methods and the experiments show that the proposed approach achieves high accuracy and generates stable models.

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Cited By

Gulfam MSeals CVargas-Nordcbeck ADozier G(2025)Lightweight Deep Convolutional Neural Network for Pavement Crack Recognition with Explainability AnalysisIntelligent Systems, Blockchain, and Communication Technologies10.1007/978-3-031-82377-0_1(1-15)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-3-031-82377-0_1
Ramani PMeena SSugumaran S(2025)Assessment of the Decay of Monuments Using Deep Learning and CNNCognitive Computing and Cyber Physical Systems10.1007/978-3-031-77075-3_28(350-363)Online publication date: 9-Feb-2025
https://doi.org/10.1007/978-3-031-77075-3_28
Zhang XHuang H(2024)Distilling Knowledge from a Transformer-Based Crack Segmentation Model to a Light-Weighted Symmetry Model with Mixed Loss Function for Portable Crack Detection EquipmentSymmetry10.3390/sym1605052016:5(520)Online publication date: 25-Apr-2024
https://doi.org/10.3390/sym16050520
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Abstract
Pavement crack detection continues to be a stubborn problem given the interference of various factors in the actual pavement and the complex topological structure of asphalt pavement. Among all the obstacles, the bottleneck of pavement crack ...
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cover image ACM Other conferences

SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

December 2018

496 pages

ISBN:9781450365390

DOI:10.1145/3287921

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SOICT: School of Information and Communication Technology - HUST
NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2018

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SoICT 2018

SoICT 2018: The Ninth International Symposium on Information and Communication Technology

December 6 - 7, 2018

Danang City, Viet Nam

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

Gulfam MSeals CVargas-Nordcbeck ADozier G(2025)Lightweight Deep Convolutional Neural Network for Pavement Crack Recognition with Explainability AnalysisIntelligent Systems, Blockchain, and Communication Technologies10.1007/978-3-031-82377-0_1(1-15)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-3-031-82377-0_1
Ramani PMeena SSugumaran S(2025)Assessment of the Decay of Monuments Using Deep Learning and CNNCognitive Computing and Cyber Physical Systems10.1007/978-3-031-77075-3_28(350-363)Online publication date: 9-Feb-2025
https://doi.org/10.1007/978-3-031-77075-3_28
Zhang XHuang H(2024)Distilling Knowledge from a Transformer-Based Crack Segmentation Model to a Light-Weighted Symmetry Model with Mixed Loss Function for Portable Crack Detection EquipmentSymmetry10.3390/sym1605052016:5(520)Online publication date: 25-Apr-2024
https://doi.org/10.3390/sym16050520
Zhang XHuang H(2024)LCSNet: Light-Weighted Convolution-Based Segmentation Method with Separable Multi-Directional Convolution Module for Concrete Crack Segmentation in DronesElectronics10.3390/electronics1307130713:7(1307)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071307
Nafaa SAshour KMohamed REssam HEmad DElhenawy MAshqar HHassan AAlhadidi T(2024)Automated Pavement Cracks Detection and Classification Using Deep Learning2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI)10.1109/ICMI60790.2024.10586098(1-5)Online publication date: 13-Apr-2024
https://doi.org/10.1109/ICMI60790.2024.10586098
Ullah ASun ZAgha Hassnain Mohsan SKhatoon AKhan AAhmad S(2024)Spliced Multimodal Residual18 Neural Network: A Breakthrough in Pavement Crack Recognition and CategorizationIEEE Access10.1109/ACCESS.2024.343259712(110781-110797)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3432597
Khlifati OBaba KTayeh B(2024)Survey of automated crack detection methods for asphalt and concrete structuresInnovative Infrastructure Solutions10.1007/s41062-024-01733-w9:11Online publication date: 27-Oct-2024
https://doi.org/10.1007/s41062-024-01733-w
Di Benedetto AFiani MGujski L(2023)U-Net-Based CNN Architecture for Road Crack SegmentationInfrastructures10.3390/infrastructures80500908:5(90)Online publication date: 6-May-2023
https://doi.org/10.3390/infrastructures8050090
Wang PWang CLiu HLiang MZheng WWang HZhu SZhong GLiu S(2023)Research on Automatic Pavement Crack Recognition Based on the Mask R-CNN ModelCoatings10.3390/coatings1302043013:2(430)Online publication date: 14-Feb-2023
https://doi.org/10.3390/coatings13020430
Zhang XHuang H(2023)PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature ExtractorApplied Sciences10.3390/app13181026313:18(10263)Online publication date: 13-Sep-2023
https://doi.org/10.3390/app131810263
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