Abstract
Cracks on surface walls may imply that a building possesses problems with its structural integrity. Evaluating these types of defects needs to be accurate to determine the condition of the building. Currently, the evaluation of surface cracks is conducted through visual inspection, resulting in occasions of subjective judgements being made on the classification and severity of the surface crack which poses danger for customers and the environment as it not being analysed objectively. Previous researchers have applied numerous classification methods, but they always stop their research at just being able to classify cracks which would not be fully useful for professionals such as surveyors. We propose building a hybrid web application that can classify the condition of a surface from images using a trained Hierarchal-Convolutional Neural Network (H-CNN) which can also decipher if the image that is being looked is a surface or not. For continuous improvement of the H-CNN’s accuracy, the application will have a feedback mechanism for users to send an email query on incorrectly classified images which will be used to retrain the H-CNN.
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References
Adlakha, D., Adlakha, D., Tanwar, R.: Analytical comparison between sobel and prewitt edge detection techniques. Int. J. Sci. Eng. Res. 7(1), 1482 (2016)
Amer, H.M., Abushaala, M.A.: Edge detection methods. In: 2015 2nd World Symposium on Web Applications and Networking (WSWAN), p. 1. IEEE, Sousse (2015)
Aslani, S., Dayan, M., Storelli, L., Filippi, M., Murino, V., Rocca, M., Sonaa, D.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 1–2 (2018)
Caltech: Home Objects dataset, 12 December 2006. Caltech: http://www.vision.caltech.edu/pmoreels/Datasets/Home_Objects_06/
Dai, J., Wu, Y.N.: Generative modeling of convolutional neural networks. In: The International Conference on Learning Representations (ICLR 2015), p. 1. The International Conference on Learning Representations (ICLR), San Diego (2015)
Danso, M.: Interview Validate Customer Requirements and Gain Advice. (D. Agyemang, Interviewer), 18 October 2018
Dorafshan, S., Thomas, R.J., Maguire, M.: SDNET2018: an annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks. Data Brief 21, 1664–1668 (2018)
Ellenberg, A., Kontsos, A., Bartoli, I., Pradhan, A.: Masonry crack detection application of an unmanned aerial vehicle. In: International Conference on Computing in Civil and Building Engineering, Florida, p. 1788 (2014)
Hoang, D.N.: Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding. Adv. Civ. Eng. 1 (2018a)
Hoang, D.N.: Image processing-based recognition of wall defects using machine learning approaches and steerable filters. Comput. Intell. Neurosci. 1 (2018b)
Hu, D., Tian, T., Yang, H., Xu, S., Wang, X.: Wall crack detection based on image processing. In: Third International Conference on Intelligent Control and Information Processing, p. 597. IEEE, Dalian (2012)
Kim, B., Cho, S.: Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors 18, 3452 (2018)
Kunal, K., Killemsetty, N.: Study on control of cracks in a structure through visual identification & inspection. IOSR J. Mech. Civil En. 11, 64 (2014)
Lucke, J., Sahani, M.: Generalized Softmax networks for non-linear component extraction. In: 17th International Conference, pp. 657–659. International Conference on Artificial Neural Networks, Porto (2007
Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.: High-resolution image classification with convolutional. In: IEEE International Geoscience and Remote Sensing Symposium, p. 2. IEEE, Fort Worth (2017)
Neale, S: Capturing Requirements. (D. Agyemang, Interviewer), 12 October 2018
O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458, 9 (2015)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 66 (1979)
Özgenel, F.Ç.: Concrete Crack Images for Classification, 15 January 2018. mendeley: https://data.mendeley.com/datasets/5y9wdsg2zt/1
Radiuk, M.P.: Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf. Technol. Manag. Sci. 20, 20–24 (2017)
Seo, Y., Shin, K.: Hierarchical convolutional neural networks for fashion image classification. Expert Syst. Appl. 116, 328–339 (2019)
Sharma, N., Vibhor, J., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 379 (2018)
da Silva, L., de Lucena, S.: Concrete cracks detection based on deep learning image classification. In: Proceedings, p. 1. Molecular Diversity Preservation International (MDPI), Brussels (2018)
Wu, R., Yan, S., Shan, Y., Dang, Q., Sun, G.: Deep image: scaling up image recognition. arXiv, 2 (2015)
Zhu, X., Bain, M.: B-CNN: branch convolutional neural network for hierarchical classification. arXiv:1709.09890 (Preprint), 2 (2017)
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Agyemang, D.B., Bader, M. (2020). Surface Crack Detection Using Hierarchal Convolutional Neural Network. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_15
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