Skip to main content
SpringerLink
Log in

Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Detecting and promptly identifying cracks on road surfaces is of paramount importance for preserving infrastructure integrity and ensuring the safety of road users, including both drivers and pedestrians. Presently, the predominant approach for crack detection relies on labor-intensive and costly manual inspections, exacerbated by the enduring challenge of concrete road cracks susceptible to environmental factors like temperature fluctuations, heavy traffic loads, and prolonged exposure to harsh weather conditions. This paper presents a comprehensive research initiative aimed at advancing road crack detection techniques. The proposed method leverages a classical Convolutional Neural Network (CNN) in conjunction with the Extreme Learning Machine (ELM) for efficient feature extraction and classification. A pivotal breakthrough is achieved by replacing the fully connected layer of the CNN with ELM, thus circumventing the time-consuming backpropagation process and significantly expediting the training process. This integration harnesses ELM’s swift learning capabilities and strong generalization performance, complemented by the CNN’s exceptional feature extraction abilities. The model’s effectiveness is rigorously assessed using the wide recognized CCIC, SDNET2018, and Synthesized datasets, and its proficiency in distinguishing between Crack and No-Crack regions is demonstrated through various performance metrics. Furthermore, the model’s performance is benchmarked against existing deep learning models using these performance metrics, showcasing its superior performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Algorithm 2
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data Availability

The dataset analyzed during the current study is publicly available.

Notes

  1. https://mathworld.wolfram.com/Moore-PenroseMatrixInverse.htm

  2. https://www.kaggle.com/datasets/arnavr10880/concrete-crack-images-for-classification

  3. www.thapar.edu

  4. www.thapar.edu

  5. https://keras.io/api/data_loading/image/

  6. https://keras.io/api/layers/activations/#relu-function

References

  1. Ji A, Xue X, Wang Y, Luo X, Xue W (2020) An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Autom Constr 114:103176

    Article  Google Scholar 

  2. Dung CV, Anh LD (2019) Autonomous concrete crack detection using deep fully convolutional neural network. Autom Constr 99:52–58

    Article  Google Scholar 

  3. Mei Q, Gül M (2020) A cost effective solution for pavement crack inspection using cameras and deep neural networks. Constr Build Mater 256:119397

    Article  Google Scholar 

  4. Zhang K, Zhang Y, Cheng H-D (2020) Crackgan: pavement crack detection using partially accurate ground truths based on generative adversarial learning. IEEE Trans Intell Transp Syst 22(2):1306–1319

    Article  MathSciNet  Google Scholar 

  5. Zou Q, Zhang Z, Li Q, Qi X, Wang Q, Wang S (2018) Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Trans Image Process 28(3):1498–1512

    Article  MathSciNet  Google Scholar 

  6. Xiao S, Shang K, Lin K, Wu Q, Gu H, Zhang Z (2023) Pavement crack detection with hybrid-window attentive vision transformers. Int J Appl Earth Obs Geoinf 116:103172

    Google Scholar 

  7. Yang F, Zhang L, Yu S, Prokhorov D, Mei X, Ling H (2019) Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans Intell Transp Syst 21(4):1525–1535

    Article  Google Scholar 

  8. Wang W, Su C (2022) Automatic classification of reinforced concrete bridge defects using the hybrid network. Arab J Sci Eng 47(4):5187–5197

    Article  Google Scholar 

  9. Chen F-C, Jahanshahi MR (2017) Nb-cnn: deep learning-based crack detection using convolutional neural network and naïve bayes data fusion. IEEE Trans Industr Electron 65(5):4392–4400

    Article  Google Scholar 

  10. Zakeri H, Nejad FM, Fahimifar A (2017) Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch Comput Methods Eng 24:935–977

    Article  Google Scholar 

  11. Kaur R, Roul RK, Batra S (2023) An efficient approach for accident severity classification in smart transportation system. Arab J Sci Eng 48(8):9645–9659

    Article  Google Scholar 

  12. Kaur R, Roul RK, Batra S (2023) A hybrid deep learning cnn-elm approach for parking space detection in smart cities. Neural Comput Appl 35(18):13665–13683

    Article  Google Scholar 

  13. Dong Y, Liu Q, Du B, Zhang L (2022) Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Trans Image Process 31:1559–1572

    Article  Google Scholar 

  14. Maniat M, Camp CV, Kashani AR (2021) Deep learning-based visual crack detection using google street view images. Neural Comput Appl 33(21):14565–14582

    Article  Google Scholar 

  15. Lanjewar MG, Gurav O (2022) Convolutional neural networks based classifications of soil images. Multimed Tools Appl 81(7):10313–10336

    Article  Google Scholar 

  16. Lanjewar MG, Parab JS (2023) Cnn and transfer learning methods with augmentation for citrus leaf diseases detection using paas cloud on mobile. Multimed Tools Appl, 1–26

  17. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  18. Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376–390

    Article  MathSciNet  Google Scholar 

  19. Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern, Part B (Cybernetics) 42(2):513–529

  20. Cao F, Yang Z, Ren J, Chen W, Han G, Shen Y (2019) Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. IEEE Trans Geosci Remote Sens 57(8):5580–5594

    Article  Google Scholar 

  21. Li H, Zhao H, Li H (2018) Neural-response-based extreme learning machine for image classification. IEEE Trans Neural Netw Learn Syst 30(2):539–552

    Article  MathSciNet  Google Scholar 

  22. Kasun LLC, Yang Y, Huang G-B, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906–3918

    Article  MathSciNet  Google Scholar 

  23. Rujirakul K, So-In C (2018) Histogram equalized deep pca with elm classification for expressive face recognition. In: 2018 International workshop on advanced image technology (IWAIT). IEEE, pp 1–4

  24. Gurpinar F, Kaya H, Dibeklioglu H, Salah A (2016) Kernel elm and cnn based facial age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 80–86

  25. Kheradmandi N, Mehranfar V (2022) A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Constr Build Mater 321:126162

    Article  Google Scholar 

  26. Oliveira H, Correia PL (2012) Automatic road crack detection and characterization. IEEE Trans Intell Transp Syst 14(1):155–168

    Article  Google Scholar 

  27. Kim B, Yuvaraj N, Sri Preethaa K, Arun Pandian R (2021) Surface crack detection using deep learning with shallow cnn architecture for enhanced computation. Neural Comput Appl 33:9289–9305

    Article  Google Scholar 

  28. Guo F, Qian Y, Liu J, Yu H (2023) Pavement crack detection based on transformer network. Autom Constr 145:104646

    Article  Google Scholar 

  29. Zhang L, Yang F, Daniel Zhang Y, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE International conference on image processing (ICIP), pp 3708–3712

  30. Zhang A, Wang KC, Fei Y, Liu Y, Chen C, Yang G, Li JQ, Yang E, Qiu S (2019) Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering 34(3):213–229

    Article  Google Scholar 

  31. Cha Y-J, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering 32(5):361–378

    Article  Google Scholar 

  32. Fan R, Bocus MJ, Zhu Y, Jiao J, Wang L, Ma F, Cheng S, Liu M (2019) Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE Intelligent vehicles symposium (IV). IEEE, pp 474–479

  33. Priyadharshini RA, Arivazhagan S, Arun M (2023) Crack recognition on concrete structures based on machine crafted and hand crafted features. Expert Syst Appl 228:120447

    Article  Google Scholar 

  34. Arya D, Maeda H, Ghosh SK, Toshniwal D, Mraz A, Kashiyama T, Sekimoto Y (2021) Deep learning-based road damage detection and classification for multiple countries. Autom Constr 132:103935

    Article  Google Scholar 

  35. Cao M-T, Tran Q-V, Nguyen N-M, Chang K-T (2020) Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Adv Eng Inform 46:101182

    Article  Google Scholar 

  36. Zhang Y, Zuo Z, Xu X, Wu J, Zhu J, Zhang H, Wang J, Tian Y (2022) Road damage detection using uav images based on multi-level attention mechanism. Autom Constr 144:104613

    Article  Google Scholar 

  37. Deepa D, Sivasangari A (2023) An effective detection and classification of road damages using hybrid deep learning framework. Multimed Tools Appl 82(12):18151–18184

    Article  Google Scholar 

  38. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2. Ieee, pp 985–990

  39. Dorafshan S, Thomas RJ, Maguire M (2018) Sdnet2018: an annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks. Data Brief 21:1664–1668

    Article  Google Scholar 

  40. Lanjewar MG, Panchbhai KG (2023) Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud. Neural Comput Appl 35(3):2755–2771

    Article  Google Scholar 

  41. Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163

    Article  Google Scholar 

  42. Lanjewar MG, Morajkar PP, Parab J (2022) Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on paas cloud. Multimed Tools Appl 81(12):16537–16562

    Article  Google Scholar 

  43. Lanjewar MG, Parab JS, Shaikh AY, Sequeira M (2023) Cnn with machine learning approaches using extratreesclassifier and mrmr feature selection techniques to detect liver diseases on cloud. Clust Comput 26(6):3657–3672

    Article  Google Scholar 

  44. Bhattacharya G, Puhan N, Mandal B (2022) Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition. Signal Process Image Commun 108:116818

    Article  Google Scholar 

  45. Miao P, Srimahachota T (2021) Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques. Constr Build Mater 293:123549

    Article  Google Scholar 

  46. Arbaoui A, Ouahabi A, Jacques S, Hamiane M (2021) Concrete cracks detection and monitoring using deep learning-based multiresolution analysis. Electronics 10(15)

  47. Zeeshan M, Adnan SM, Ahmad W, Khan FZ (2021) Structural crack detection and classification using deep convolutional neural network. Pakistan Journal of Engineering and Technology 4(4):50–56

    Article  Google Scholar 

  48. He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  49. Zhong H, Miao C, Shen Z, Feng Y (2014) Comparing the learning effectiveness of bp, elm, i-elm, and svm for corporate credit ratings. Neurocomputing 128:285–295

    Article  Google Scholar 

  50. Lanjewar MG, Parab JS, Shaikh AY, Sequeira M (2023) Cnn with machine learning approaches using extratreesclassifier and mrmr feature selection techniques to detect liver diseases on cloud. Clust Comput 26(6):3657–3672

    Article  Google Scholar 

  51. Lanjewar MG, Panchbhai KG, Charanarur P (2023) Lung cancer detection from ct scans using modified densenet with feature selection methods and ml classifiers. Expert Syst Appl 224:119961

    Article  Google Scholar 

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, Punjab, India

    Navpreet, Rajendra Kumar Roul & Rinkle Rani

Authors
  1. Navpreet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajendra Kumar Roul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rinkle Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Navpreet.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare that any kind of personal or financial relationship with other people or organizations does not influence the interpretation of data used in this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Navpreet, Roul, R.K. & Rani, R. Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19291-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19291-9

Keywords

Search

Navigation