1,089
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Gravel road classification based on loose gravel using transfer learning

, &
Article: 2138879 | Received 21 Jun 2022, Accepted 17 Oct 2022, Published online: 11 Nov 2022

References

  • Abu Daoud, O., et al., 2021. Validating the practicality of utilising an image classifier developed using TensorFlow framework in collecting corrugation data from gravel roads. International Journal of Pavement Engineering, May, 1–12. doi:10.1080/10298436.2021.1921773
  • Abulizi, N., et al., 2016. Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS. Journal of Traffic and Transportation Engineering (English Edition), 3 (5), 398–411. doi:10.1016/j.jtte.2016.09.004.
  • Albatayneh, O., et al., 2020. Complementary modeling of gravel road traffic-generated dust levels using Bayesian regularization feedforward neural networks and binary probit regression. International Journal of Pavement Research and Technology, 13 (3), 255–262. doi:10.1007/s42947-020-0261-3.
  • Albatayneh, O., Forslöf, L., and Ksaibati, K., 2019. Developing and validating an image processing algorithm for evaluating gravel road dust. International Journal of Pavement Research and Technology, 12 (3), 288–296. doi:10.1007/s42947-019-0035-y.
  • Allouch, A., et al., 2017. Roadsense: smartphone application to estimate road conditions using accelerometer and gyroscope. IEEE Sensors Journal, 17 (13), 4231–4238. doi:10.1109/JSEN.2017.2702739.
  • Alzubaidi, H., 1999. Operation and maintenance of gravel roads: A literature study. http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A673330&dswid=−4629.
  • Alzubaidi, H., 2001. On rating of gravel roads. PhD dissertation. KTH.
  • Anguelov, D., et al., 2010. Google street view: capturing the world at street level. Computer, 43 (6), 32–38. doi:10.1109/MC.2010.170.
  • Chicco, D. and Jurman, G., 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21 (1), 6. doi:10.1186/s12864-019-6413-7.
  • Cord, A. and Chambon, S., 2012. Automatic road defect detection by textural pattern recognition based on AdaBoost. Computer-Aided Civil and Infrastructure Engineering, 27 (4), 244–259. doi:10.1111/j.1467-8667.2011.00736.x.
  • Dhananjay Theckedath, R.R.S., 2020. Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science. doi:10.1007/s42979-020-0114-9
  • Forslöf, L. and Jones, H., 2015. Roadroid: continuous road condition monitoring with smart phones. Journal of Civil Engineering and Architecture, 9 (4), 485–496. doi:10.17265/1934-7359/2015.04.012.
  • George Karimpanal, Thommen and Bouffanais, R., 2019. Self-organizing maps for storage and transfer of knowledge in reinforcement learning. Adaptive Behavior, 27 (2), 111–126.
  • Gopalakrishnan, K., et al., 2017. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330. doi:10.1016/j.conbuildmat.2017.09.110.
  • Gorges, C., Öztürk, K., and Liebich, R., 2019. Impact detection using a machine learning approach and experimental road roughness classification. Mechanical Systems and Signal Processing, 117, 738–756. doi:10.1016/j.ymssp.2018.07.043.
  • Hameed, Z., et al., 2020. Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors, 20 (16), 4373–17. doi:10.3390/s20164373.
  • Hossein Alzubaidi, 2014. Bedömning av grusväglag (Assesment of gravel roads),TDOK 2014:0135 Version 1.0, Trafikverket. https://trafikverket.ineko.se/Files/sv-SE/10845/RelatedFiles/2005_060_bedomning_av_grusvaglag.pdf.
  • Howard, J. and Ruder, S., 2018. Universal language model fine-tuning for text classification. Acl 2018 – 56th annual meeting of the association for computational linguistics, proceedings of the conference (long papers), 1, 328–339. doi:10.18653/v1/p18-1031
  • Iandola, F., et al., 2014. Densenet: Implementing efficient convnet descriptor pyramids. ArXiv Preprint ArXiv:1404.1869.
  • Kanhere, S.S., 2011. Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. Ieee 12th international conference on mobile data management, 6–9 June, Lulea, Sweden, 3–6. doi:10.1109/MDM.2011.16.
  • Kans, M., Campos, J., and Håkansson, L., 2020. Smart innovation, systems and technologies. Advances in Asset Management and Condition Monitoring, 166, 451–461.
  • Karin Edvardsson, Thomas Lundberg L. S., 2015. Objektiv mätmetod för tillståndsbedömning av grusväglag, VTI rapport 863 Objektiv.
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.
  • Mednis, A., et al., 2011. Real time pothole detection using Android smartphones with accelerometers. International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), (June 2014), 1–6. doi:10.1109/DCOSS.2011.5982206
  • Mushtaq, Z., Su, S.F., and Tran, Q.V., 2021. Spectral images based environmental sound classification using CNN with meaningful data augmentation. Applied Acoustics, 172, 107581. doi:10.1016/j.apacoust.2020.107581.
  • Nervis, L.O. and Nuñez, W.P., 2019. Identification and discussion on distress mechanisms of unsurfaced gravel roads. International Journal of Pavement Research and Technology, 12 (1), 88–96. doi:10.1007/s42947-019-0011-6.
  • Nyberg, R.G., 2016. Automating condition monitoring of vegetation on railway trackbeds and embankments. Edinburgh University.
  • Nyberg, R.G., Yella, S., and Dougherty, M., 2015. Inter-rater reliability in determining the types of vegetation on railway trackbeds. In Web information systems engineering – WISE 2015: 16th international Conference, Miami, FL, USA. Springer, Cham.
  • Paszke, A., et al., 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32.
  • Patrick, C., and Foedisch, M., 2003. Performance evaluation of color based road detection using neural nets and support vector machine. 32nd applied imagery pattern recognition workshop. doi:10.1109/AIPR.2003.1284265
  • Rajab, M.I., Alawi, M.H. and Saif, M.A., 2008. Application of image processing to measure road distresses. WSEAS Transactions on Information Science and Applications, 5 (1), 1–7.
  • Russakovsky, Olga, et al., 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115 (3), 211–252.
  • Saeed, N., et al., 2020. A review of intelligent methods for unpaved roads condition assessment. In: 2020 15th IEEE conference on industrial electronics and applications (ICIEA), May, 79–84.
  • Saeed, N., et al., 2021. Classification of the acoustics of loose gravel. Sensors, 21 (14). doi:10.3390/s21144944.
  • Simonyan, K. and Zisserman, A., 2016. Very deep convolutional neural networks. ICLR 2015, July, Alessandro Luca Vilardi. https://www.robots.ox.ac.uk/~vgg/research/very_deep/.
  • Sodikov, J., Tsunokawa, K., and Ul-Islam, R., 2005. Road survey with ROMDAS system: A study in akita prefecture. Departmental Bulletin Paper, (February), 149–151.
  • Transfer learning for computer vison tutorial, n.d. https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#convnet-as-fixed-feature-extractor.
  • Wang, H. W., et al., 2015. A real-time pothole detection approach for intelligent transportation system. Mathematical Problems in Engineering.
  • Wardhani, N.W.S., et al., 2019. Cross-validation metrics for evaluating classification performance on imbalanced data. In: 2019 international conference on computer, control, informatics and Its applications: emerging trends in Big data and artificial intelligence, IC3INA 2019, 14–18. doi:10.1109/IC3INA48034.2019.8949568
  • Weiss, K., Khoshgoftaar, T.M., and Wang, D., 2016. A survey of transfer learning. Journal of Big Data, 3 (1), 9. doi:10.1186/s40537-016-0043-6.
  • Zhang, C. and Elaksher, A., 2012. An unmanned aerial vehicle-based imaging system for 3D measurement of unpaved road surface distresses. Computer-Aided Civil and Infrastructure Engineering, 27 (2), 118–129. doi:10.1111/j.1467-8667.2011.00727.x.
  • Zhang, F., Bales, C., and Fleyeh, H., 2021. From time series to image analysis: A transfer learning approach for night setback identification of district heating substations. Journal of Building Engineering, 43, 102537.