Abstract
Mechanized labeling of pavement distress is of preponderant usefulness in transportation segment for warrant of safety. Typically, non-automated techniques are obligatory for conventional classification algorithms, thus having constrained breadth of usage. In the matter herein presents a modus operandi for finding and classifying pavement distress on road which makes use of a deep neural network technique called as convolutional neural network (CNN) to classify the given images of distress into their different categories by making use of “activation function” to proclaim distinct identification of likely features by selecting the features automatically. A comparative result is given for three activation functions, viz. ReLU (Rectified Linear Unit), Sigmoid, and Tanh. Denouement from the results herein points out that ReLU surpasses Sigmoid and Tanh. Amidst Sigmoid and Tanh, Tanh furnishes exceeding accomplishment in terms of time.
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References
Hu, Y., Zhao, C.x.: A novel lbp based methods for pavement crack detection. Journal of pattern Recognition research 5(1) (2010) 140–147.
Salman, M., Mathavan, S., Kamal, K., Rahman, M.: Pavement crack detection using the gabor lter. In: Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on, IEEE (2013) 2039–2044.
Huidrom, L., Das, L.K., Sud, S.: Method for automated assessment of potholes, cracks and patches from road surface video clips. Procedia-Social and Behavioral Sciences 104 (2013) 312–321.
Jahanshahi, M.R., Masri, S.F., Padgett, C.W., Sukhatme, G.S.: An innovative methodology for detection and quanti cation of cracks through incorporation of depth perception. Machine vision and applications (2013) 1–15.
Oliveira, H., Correia, P.L.: Automatic road crack detection and characterization. IEEE Transactions on Intelligent Transportation Systems 14(1) (2013) 155–168.
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016).
Schmidhuber, J.: Deep learning in neural networks: An overview. Neural networks 61 (2015) 85–117.
Nielsen, M.A.: Neural networks and deep learning (2015).
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial pointdetection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2013) 3476–3483.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classi cation with deep convolutional neural networks. In: Advances in neural information processing systems. (2012) 1097–1105.
Levi, G., Hassner, T.: Age and gender classi cation using convolutional neuralnetworks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. (2015) 34–42.
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. ar**v preprint ar**v:1412.6980 (2014).
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from over tting. Journal of Machine Learning Research 15(1) (2014) 1929–1958.
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Recti er nonlinearities improve neural network acoustic models. In: Proc. ICML. Volume 30. (2013).
Rojas, R.: The backpropagation algorithm. In: Neural networks. Springer (1996) 149–182.
Kalman, B.L., Kwasny, S.C.: Why tanh: choosing a sigmoidal function. In: Neural Networks, 1992. IJCNN., International Joint Conference on. Volume 4., IEEE (1992) 578–581.
Ozkan, C., Erbek, F.S.: The comparison of activation functions for multispectral landsat tm image classi cation. Photogrammetric Engineering & Remote Sensing 69(11) (2003) 1225–1234.
Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Arti cial Intelligence and Expert Systems 1(4) (2011) 111–122.
Bishop, C.M.: Pattern recognition and machine learning. springer (2006).
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Pandey, S., Dholay, S. (2019). An Image Processing Approach for Analyzing Assessment of Pavement Distress. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 32. Springer, Singapore. https://doi.org/10.1007/978-981-10-8201-6_55
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DOI: https://doi.org/10.1007/978-981-10-8201-6_55
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