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Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
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  • Korea Citation Index (KCI)
1. 
Zhang, L., Yang, F., Zhang, Y.D., Zhu, Y.J. (2016), Road crack detection using deep convolutional neural network, In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–-28 September 2016; pp. 3708-3712.DOI
2. 
FENG, C., Liu, M.-Y., Kao, C.-C., Lee, T.-Y. (2017), Deep Active Learning for Civil Infrastructure Defect Detection and Classification, International Workshop on Computing in Civil Engineering (IWCCE), pp. 298-306.DOI
3. 
Eisenbatch, Markus (2017), How to get pavement distress detection ready for deep learning? A systematic approach, 2017 international joint conference on neural networks (IJCNN). IEEE, pp. 2039-2047.DOI
4. 
Loffe, S., Szegedy, C. (2015), Batch normalization: Acceleration deep network training by redusing internal covariate shift, arXiv preprint arXiv: 1502.03167.Google Search
5. 
Pauly, L., Hogg, D., Fuentes, R., Peel, H. (2017), Deeper networks for pavement crack detection, In Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC). IAARC, pp. 479-485.DOI
6. 
Wu, J. (2017), Introduction to convolutional neural networks. National Key Lab for Novel Software Technology, Nanjing University. China, 5-23.Google Search
7. 
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P. T. P. (2016), On large-batch training for deep learning: Generalization gap and sharp minima, arXiv preprint arXiv; 1609.04836.Google Search
8. 
Ruder, S. (2016), An overview of gradient descent optimization algorithms, arXiv preprint arXiv: 16090.04747.Google Search
9. 
Gopalakrishnan, K. (2018), Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review. Data 3(3), 28.DOI
10. 
Kim, J. H., Kim, J. R., Moon, H. C. (2008), Development of Pavement Distress Survey System, Journal of Korean Society of Road Engineers (KSRE), pp. 475-484.Google Search
11. 
Choi, Y. S. (2019), Concrete Surface Defect Detection using Convolutional Neural Network, Department of Architectural Engineering Graduate School, Chungbuk National University.Google Search
12. 
Radford, A., Metz, L., Chintala, S. (2015), Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint arXiv:1511.06434.Google Search
13. 
Powers, D. M. (2011), Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.Google Search
14. 
Bengio, Y., Goodfellow, I. J., Courville, A. (2015), Deep learning, book in preparation for mit press, Disponıvel em http://www. iro. umontreal. ca/bengioy/dlbook.Google Search
15. 
Goodfellow, I., Bengio, Y., Courville, A. (2016), Deep learning, MIT press, pp. 321-362.Google Search
16. 
Hu, J., Shen, L., Sun, G. (2018), Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).DOI
17. 
Rababaah, H., Vrajitoru, D., Wolfer, J. (2005), Asphalt pavement crack classification: a comparison of GA, MLP, and SOM, In Proceedings of Genetic and Evolutionary Computation Conference, Late-Breaking Paper.Google Search
18. 
Koch, C., Brilakis, I (2011), Pothole detection in asphalt pavement images, Advanced Engineering Informatics, 25(3), pp. 507-515.DOI
19. 
Sorncharean, S., Phiphobmongkol, S. (2008), Crack detection on asphalt surface image using enhanced grid cell analysis, In 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008), pp. 49-54.DOI