References
- Al Angelova, A., Krizhevsky, A., and Vanhoucke, V., 2015. Pedestrian detection with a large-field-of-view deep network. In: 2015 IEEE international conference on robotics and automation (ICRA), Seattle, WA, USA, 704–711.
- Albatayneh, Omar, Forslof, Lars, and Ksaibati, Khaled, 2019. Developing and validating an image processing algorithm for evaluating gravel road dust. International Journal of Pavement Research and Technology, 12, 288–296.
- Albatayneh, Omar, Forslof, Lars, and Ksaibati, Khaled, 2020. Image retraining using TensorFlow implementation of the pretrained inception-v3 model for evaluating gravel road dust. Journal of Infrastructure Systems, 26 (2), 1–10.
- Aleadelat, W., Wulff, S., and Ksaibati, K, 2019. Development of performance prediction models for gravel roads using Markov chains. American Journal of Civil Engineering, 7 (3), 73–81.
- Alipour, M., Harris, D. K., and Miller, G. R, 2019. Robust Pixel-level Crack detection using deep fully convolutional neural networks. Journal of Computing in Civil Engineering, 33 (6), 04019040.
- Al-Suleiman (Obaidat), Turki I., and Abu Daoud, Osama, 2021. Evaluation of pavement condition of the primary roads in Jordan using SHRP procedure. Jordan Journal of Civil Engineering, 15 (2), 305–317.
- Bengio, Y, 2009. Learning deep architectures for AI. Foundations and Trends® in Machine Learning, 2 (1), 1–127.
- Chen, L.C., et al., 2018. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834–848.
- Eaton, R. A, and Beaucham, R.E, 1992. Unsurfaced road maintenance management. , Hanover, NH: United States Army Corps of Engineers Cold Regions Research and Engineering Laboratory, special report 92-26.
- Elnaz Jahani, Heravi, Aghdam, Hamed Habibi, and Puig, Domenec, 2018. An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods. Pattern Recognition Letters, 2018 (105), 50–58.
- Feng, Q., Peng, D., and Gu, Y., 2019. Research of regularization techniques for SAR target recognition using deep CNN models. In: Vol. 11069 of proceedings of 10th international conference on graphics and image processing (ICGIP 2018), 110693P. Xi’an: International Conference on Graphics And Image Processing.
- Frome, A., et al., 2017. Devise: a deep visual-semantic embedding model. In: Advances in neural information processing system, 2121–2129. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41473.pdf
- Gong, H., et al., 2019. Neural networks for fatigue cracking prediction using outputs from pavement mechanistic-empirical design. International Journal of Pavement Engineering, 22 (2),162–172.
- Gu, J., et al., 2018. Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
- Huntington, G., and Ksaibati, K., 2009. Annualized road works cost estimates for unpaved roads. Journal of Transportation Engineering, 135 (10), 702–710.
- Lasi, H., et al., 2014. Industry 4.0. Business & Information Systems Engineering, 6, 239–242.
- Li, S., Cao, Y., and Cai, H., 2017a. Automatic pavement-crack detection and segmentation based on steerable matched filtering and an active contour model. Journal of Computing in Civil Engineering, 31, 04017045.
- Li, G., et al., 2017b. Bt-nets: simplifying deep neural networks via block term decomposition. arXiv: 1712.05689.
- Libbrecht, M. W., and Noble, W. S, 2015. Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16 (6), 321–332.
- Mazzini, Davide, et al., 2020. A novel approach to data augmentation for pavement distress segmentation. Computers in Industry, 121, 1–13.
- McCarthy, J., et al., 2006. Proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27 (4), 12.
- Nguyen, H. T., Nguyen, L. T., and Sidorov, D. N., 2016. A robust approach for road pavement defects detection and classification. Journal of Computational and Engineering Mathematics, 3 (3), 40–52.
- Nie, M., and Wang, K, 2018. Pavement distress detection based on transfer learning. In: 2018, 5th international conference on systems and informatics (ICSAI), Nanjing, China, 435–439.
- Schwartz, E., et al., 2018. Delta-encoder: an effective sample synthesis method for few-shot object recognition. arXiv preprint arXiv: 1806.04734.
- Szegedy, C., et al., 2016. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2818-2826.
- Transportation Information Center at University of Wisconsin-Madison, 2002. Pavement Surface Evaluation and Rating-PASER Manual Gravel roads.
- Wan, L., et al., 2013. Regularization of neural networks using drop-connect. In: International conference on machine learning, 1058–1066.
- Wang, X., and Hu, Z, 2017. Grid-based pavement crack analysis using deep learning. In: 2017 4th international conference on transportation information and safety (ICTIS), Banff, AB, Canada, 917–924.
- Warrier, N. K., and Sathish, K, 2018. Object detection on roads using deep learning and neural networks. Journal of Network Communications and Emerging Technologies (JNCET), 8 (4), 325–328.
- Wyoming Technology Transfer Center/ Local Technical Assistance Program (WYT2/LTAP), 2014. Gravel Roads Rating System, Rev. 1.4.
- Xia, X., Xu, C., and Nan, B, 2017. Inception-v3 for flower classification. In: 2017 2nd International Conference on image, vision and Computing (ICIVC), Chengdu, China, 783–787.
- Yao, X., Yao, M., and Xu, B., 2008. Automated detection and identification of area-based distress in concrete pavements. In: Seventh International Conference on managing pavement Assets. Calgary, AL: The National Academies of Sciences, Engineering, and Medicine.
- Yang, J., Lu, J. J., and Gunaratne, M., 2003. Application of neural network models for forecasting of pavement crack index and pavement condition rating. Final Report. Tampa, FL: College of Engineering, Univ. of South Florida.
- Yousaf, Muhammad Haroon, et al., 2018. Visual analysis of asphalt pavement for detection and localization of potholes. Advanced Engineering Informatics, 38, 527–537.
- Zhang, Q., et al., 2019. Recent advances in convolutional neural network acceleration. Neuro-Computing, 2019 (323), 37–51.