Abstract
Vehicle tires must be maintained to assure performance, efficiency, and safety. Though vehicle owners may monitor tread depth and air pressure, most are unaware of the safety risks of degrading rubber. This paper identifies the need for tire material condition monitoring and develops a densely connected convolutional neural network to identify cracking from smartphone photographs. This model attains an accuracy of 81.2% on cropped outsample images, besting inexperienced humans’ 55% performance. We develop a web service using this model as the basis of an AI-backed “Diagnostics-as-a-Service” platform for online vehicle condition assessment. By encoding knowledge of visual risk indicators into a neural network model operable from a user’s trusted smartphone, we raise awareness of the risk of degraded rubber and improve vehicle safety without requiring specialized operator training.
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References
Baldwin, J.M., Bauer, D.R.: Rubber oxidation and tire aging - a review. Rubber Chem. Technol. 81(2), 338–358 (2008). https://doi.org/10.5254/1.3548213
Cowley, J.A., Kim, S., Wogalter, M.S.: People do not identify tire aging as a safety hazard. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, pp. 860–864. Sage Publications, Los Angeles (2006)
Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2584–2589, July 2016
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016). http://arxiv.org/abs/1608.06993
Janssens, O., de Walle, R.V., Loccufier, M., Hoecke, S.V.: Deep learning for infrared thermal image based machine health monitoring. IEEE/ASME Trans. Mechatron. PP(99), 1 (2017)
Kalsher, M.J., Wogalter, M.S., Laughery, K.R., Lim, R.W.: Consumer knowledge of tire maintenance and aging hazard. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 49, pp. 1757–1757. SAGE Publications, Los Angeles (2005)
Liu, L., Chen, J., Fieguth, P.W., Zhao, G., Chellappa, R., Pietikäinen, M.: A survey of recent advances in texture representation. CoRR abs/1801.10324 (2018). http://arxiv.org/abs/1801.10324
Liu, Z., Ukida, H., Niel, K., Ramuhalli, P.: Industrial Inspection with Open Eyes: Advance with Machine Vision Technology, pp. 1–37. Springer, London (2015). https://doi.org/10.1007/978-1-4471-6741-9_1
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. CoRR abs/1712.04621 (2017), http://arxiv.org/abs/1712.04621
Siegel, J., Bhattacharyya, R., Sarma, S., Deshpande, A.: Smartphone-Based Vehicular Tire Pressure and Condition Monitoring, pp. 805–824. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56994-9_56
Siegel, J.E., Bhattacharyya, R., Kumar, S., Sarma, S.E.: Air filter particulate loading detection using smartphone audio and optimized ensemble classification. Eng. Appl. Artif. Intell. 66(Supplement C), 104–112 (2017). http://www.sciencedirect.com/science/article/pii/S0952197617302294
Siegel, J.E., Bhattacharyya, R., Sarma, S., Deshpande, A.: Smartphone-based wheel imbalance detection. In: ASME 2015 Dynamic Systems and Control Conference, American Society of Mechanical Engineers (2015)
Sivinski, R.: Evaluation of the effectiveness of TPMS in proper tire pressure maintenance. Technical report, NHTSA (2012)
Zhao, G., Zhang, G., Ge, Q., Liu, X.: Research advances in fault diagnosis and prognostic based on deep learning. In: 2016 Prognostics and System Health Management Conference (PHM-Chengdu), pp. 1–6, October 2016
Zipperle, M., Malassa, R., Rief, B.: Climatic influences on the ageing of car tyres. Int. Polym. Sci. Technol. 35(6), T1 (2008)
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The Titan Xp used for this research was donated by the NVIDIA Corporation.
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Siegel, J.E., Sun, Y., Sarma, S. (2018). Automotive Diagnostics as a Service: An Artificially Intelligent Mobile Application for Tire Condition Assessment. In: Aiello, M., Yang, Y., Zou, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2018. AIMS 2018. Lecture Notes in Computer Science(), vol 10970. Springer, Cham. https://doi.org/10.1007/978-3-319-94361-9_13
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DOI: https://doi.org/10.1007/978-3-319-94361-9_13
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