skip to main content
10.1145/3654823.3654912acmotherconferencesArticle/Chapter ViewAbstractPublication PagescacmlConference Proceedingsconference-collections
research-article

Images Data Set Improvement Using Mathematical Morphology Operators

Published: 29 May 2024 Publication History

Abstract

Earthquakes generate the world's most significant hazards, being caused by tectonic movements which generate a certain amount of energy called shock waves which are dissipated into the earth. The presence of visual effects, such as tsunamis, building cracks, and collapsing bridges, is contingent upon the intensity, while the absence of any visual effects indicates no damage. This research aims to evaluate the impact of mathematical morphology operators (MMO) on an image dataset depicting structural defects (cracks) caused by earthquakes in civil works. To evaluate this effect, two artificial neural network architectures (VGG16 and VGG19) were assessed on this dataset. The scope is to detect if the analyzed images present sign of structural defects (cracks). Depending on the cracks size the structure of a building can be compromised or not. A compromised building can represent a danger for humans who use it in case of building failure. The efficacy of VGG16 and VGG19 models was obtained using performance metrics including accuracy and confusion matrix. Each artificial network architecture used is discussed being highlighted the MMO improvement.

References

[1]
Mohammad Rahai, Akbar Esfandiari, Ali Bakhshi. 2020. Detection of structural damages by model updating based on singular value decomposition of transfer function subsets. Struct. Control Health Monit.
[2]
Yung-An Hsieh and Yichang James Tsai. 2020. Machine learning for crack detection: Review and model performance comparison. J. Comput. Civ. Eng.
[3]
Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari.2021. White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks. Journal of Electronic Imaging.
[4]
Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald & Edin Muharemagic. 2015. Deep learning applications and challenges in big data analytics. J. Big Data.
[5]
Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Netw.
[6]
Jieun Baek, and Yosoon Choi. 2020. Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines. Applied Sciences.
[7]
Xavier Glorot, Antoine Bordes, Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, vol.15, pp. 315-323
[8]
Mohammed Oualid Attaoui, Hazem Fahmy, Fabrizio Pastore, Lionel Briand. 2022. Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering. ACM Transactions on Software Engineering and Methodology.
[9]
Ganesh Kolappan Geetha, Sung-Han Sim. 2022. Fast identification of concrete cracks using 1D deep learning and explainable artificial intelligence-based analysis. Automation in Construction.
[10]
Zaid Al-Huda, Bo Peng, Riyadh Nazar Ali Algburi, Saghir Alfasly & Tianrui Li. 2022. Weakly supervised pavement crack semantic segmentation based on multi-scale object localization and incremental annotation refinement. Applied Intelligence.
[11]
Xincong Yang, Heng Li, Yantao Yu, Xiaochun Luo, Ting Huang, Xu Yang. 2018. Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network. Comput. Civ. Infrastruct. Eng.
[12]
Yuki Inoue, Hiroto Nagayoshi. 2021. Crack detection as a weakly-supervised problem: towards achieving less annotation-intensive crack detectors. International conference on pattern recognition (ICPR).
[13]
Jessie Li. 2023. Asymptotics of K-Fold Cross Validation. Journal of Artificial Intelligence Research, Vol. 78 (2023).
[14]
Kasthurirangan Gopalakrishnan, Siddhartha K. Khaitan, Alok Choudhary, Ankit Agrawal. 2017. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, Volume 157, Pages 322-330.
[15]
Sylvie Chambon and Jean-Marc Moliard, 2011. Automatic Road pavement assessment with image processing: review and comparison. Int. J. Geophys, Volume 2011 | Article ID 989354.
[16]
Ayenu-Prah, and N. Attoh-Okine, 2008. Evaluating pavement cracks with bidimensional empirical mode decomposition. EURASIP J. Adv. Signal Process. 2008, 861701 (2008).
[17]
Dumitru Abrudan, Ana-Maria Drăgulinescu, Radu-Ovidiu Preda, Nicolae Vizireanu. 2023. Fuel burn reduction in commercial aviation using mathematical morphology. Proc. SPIE 12493, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI, 124932Y
[18]
Çağlar Fırat Özgenel, 2018. Concrete Crack Images for Classification. Mendeley Data, Version 2.
[19]
Karen Simonyan, Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 1409.1556.
[20]
Ishak Pacal, Ahmet Karaman, Dervis Karaboga, Bahriye Akay, Alper Basturk, Ufuk Nalbantoglu, Seymanur Coskun. 2021. An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets. Computers in Biology and Medicine, Volume 141, February 2022, 105031.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
CACML '24: Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
March 2024
478 pages
ISBN:9798400716416
DOI:10.1145/3654823
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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2024

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

CACML 2024

Acceptance Rates

Overall Acceptance Rate 93 of 241 submissions, 39%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 10
    Total Downloads
  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)2
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media