Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt

( Vol-5,Issue-12,December 2018 ) OPEN ACCESS

Suayder Milhomem, Tiago da Silva Almeida, Warley Gramacho da Silva, Edeilson Milhomem da Silva, Rafael Lima de Carvalho


Distress detection, Transfer Learning, Wisard.


The present paper shows a solution to the problem of automatic distress detection, more precisely the detection of holes in paved roads. To do so, the proposed solution uses a weightless neural network known as Wisard to decide whether an image of a road has any kind of cracks. In addition, the proposed architecture also shows how the use of transfer learning was able to improve the overall accuracy of the decision system. As a verification step of the research, an experiment was carried out using images from the streets at the Federal University of Tocantins, Brazil. The architecture of the developed solution presents a result of 85.71% accuracy in the dataset, proving to be superior to approaches of the state-of-the-art.

ijaers doi crossref DOI:


Paper Statistics:
  • Total View : 215
  • Downloads : 28
  • Page No: 294-299
Cite this Article:
Click here to get all Styles of Citation using DOI of the article.

[1] S. Parfitt, “An introduction to neural computing by Igor Aleksander and Helen Morton, Chapman and Hall, London, 1990, pp 255, £15.95,” Knowl. Eng. Rev., vol. 6, no. 04, p. 351, 1991.
[2] Y. O. Ouma and M. Hahn, “Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform,” Advanced Engineering Informatics, vol. 30, no. 3, pp. 481–499, 2016.
[3] S. C. Radopoulou and I. Brilakis, “Patch detection for pavement assessment,” Autom. Constr., vol. 53, pp. 95–104, 2015.
[4] K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection,” Construction and Building Materials, vol. 157, pp. 322–330, 2017.
[5] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” CoRR, vol. abs/1409.1556, 2014.
[6] H. Nhat-Duc, Q.-L. Nguyen, and V.-D. Tran, “Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network,” Autom. Constr., vol. 94, pp. 203–213, 2018.
[7] K. Zhang, H. D. Cheng, and B. Zhang, “Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning,” J. Comput. Civ. Eng., vol. 32, no. 2, p. 04018001, 2018.
[8] Jia Deng et al., “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
[9] C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2016.
[10] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity. 1943,” Bull. Math. Biol., vol. 52, no. 1–2, pp. 99–115; discussion 73–97, 1990.
[11] Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989.
[12] Y. LeCun, Y. Bengio, and Others, “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks, vol. 3361, no. 10, p. 1995, 1995.
[13] S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, 2010.
[14] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2012.
[15] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[16] F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[17] “A Brief Introduction to Artificial Neural Networks,” in Artificial Adaptive Systems in Medicine New Theories and Models for New Applications, 2012, pp. 5–11.
[18] N. Nedjah, F. M. G. França, M. De Gregorio, and L. de Macedo Mourelle, “Weightless neural systems,” Neurocomputing, vol. 183, pp. 1–2, 2016.