Pavement Crack Detection by Convolutional AdaBoost Architecture

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In pavement structures, the occurrence and propagation of cracks are critical elements that affect road performance, health, and pose a potential threat to the safety of vehicles. The pavement condition information usually collected by manual inspection is laborious, time-consuming, inspector dependent, and easily vulnerable to the perspicacity of the inspector. Therefore, this paper aims to present a computer vision-based crack detection system for pavement structures. In the proposed work, a Convolutional Neural Network (CNN) is used for feature extraction and an AdaBoost classifier is used for classification. The combined architecture is named the Convolutional AdaBoost architecture. A comparative study was conducted and the performance of various classifiers, such as Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Ada-Boost, and Softmax, was evaluated using different evaluation metrics. The dataset was created based on various pavement structures in the United Arab Emirates and consists of 5600 crack and non-crack images. From the experimental results, it is concluded that the combined CNN features and Ada-Boost classifier yield the best performance with an accuracy of 98%. The performance of other classifiers with CNN features is also comparable with each other. Overall, the integrated Convolutional AdaBoost improves pavement crack detection performance and is more accurate than using CNN alone.

Original languageEnglish
Title of host publicationZEMCH 2021 - 8th Zero Energy Mass Custom Home International Conference, Proceedings
EditorsKheira Anissa Tabet Aoul, Mohammed Tariq Shafiq, Daniel Efurosibina Attoye
PublisherZEMCH Network
Number of pages12
ISBN (Electronic)9789948310006
Publication statusPublished - 2021
Event8th Zero Energy Mass Custom Home International Conference, ZEMCH 2021 - Dubai, United Arab Emirates
Duration: Oct 26 2021Oct 28 2021

Publication series

NameZEMCH International Conference
ISSN (Electronic)2652-2926


Conference8th Zero Energy Mass Custom Home International Conference, ZEMCH 2021
Country/TerritoryUnited Arab Emirates


  • Automatic inspection
  • Computer vision
  • Convolutional neural networks
  • Crack detection
  • Sliding window

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality
  • Building and Construction
  • Architecture
  • Renewable Energy, Sustainability and the Environment
  • Computer Science Applications


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