Deep Learning Model for Automated Tire Crack Detection Using Convolutional Neural Networks
DOI:
https://doi.org/10.15408/aism.v8i1.46226Keywords:
Tire Crack Detection, CNN, Deep Learning, Image Processing, Automated InspectionAbstract
Tire cracks pose a significant safety risk, as undetected defects can lead to severe accidents. Traditional inspection methods rely on manual visual assessments, which are prone to human error. This study proposes an automated tire crack detection system using Convolutional Neural Networks (CNN), leveraging transfer learning techniques to improve accuracy and generalization. A dataset of 600 tire images was collected and preprocessed, including augmentation techniques such as rotation, flipping, and brightness adjustments. The CNN model was trained with different optimizers, including Adam and Stochastic Gradient Descent (SGD), to compare their performance. Experimental results indicate that Adam achieved the highest test accuracy of 78.3% with the lowest test loss of 53%, while SGD required more epochs to reach optimal performance. This study demonstrates the feasibility of deep learning-based automated tire inspection, providing a scalable alternative to traditional methods. Future research should focus on optimizing model architectures, expanding datasets, and integrating real-time detection for industrial applications.
Downloads
References
Adathodi, L., Raja Murugadoss, J., & Gaddam, K. (2018). A comparative study on vehicular tyre rubber and aircraft tyre rubber: A review based on SEM eds and XRD analysis. In International Journal of Mechanical and Production Engineering Research and Development (Vol. 8, Issue 2, pp. 1227–1234). Transstellar Journal Publications and Research Consultancy Private Limited (TJPRC). https://doi.org/10.24247/ijmperdapr2018141
Anggara, M., Firda Utami, S., & Bulan Januari, D. (n.d.). Science and Technology PENGARUH VARIASI HEAT ABSORBER PLATE TERHADAP PERFORMANCE SOLAR DRYER KEMIRI (Vol. 4, Issue 1). http://jurnal.uts.ac.id
Demasi, F., Loprencipe, G., & Moretti, L. (2018). Road safety analysis of urban roads: Case study of an Italian municipality. Safety, 4(4). https://doi.org/10.3390/safety4040058
Fang, Z., Wang, Y., Peng, L., & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers and Geosciences, 139. https://doi.org/10.1016/j.cageo.2020.104470
Fathi, H., El-Sayegh, Z., & Ghoreishy, M. H. R. (2024). Prediction of rolling resistance and wheel force for a passenger car tire: A comparative study on the use of different material models and numerical approaches. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. https://doi.org/10.1177/09544070241244556
Kruger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., Rodriguez-Sanchez, A. J., & Wiskott, L. (2013). Deep hierarchies in the primate visual cortex: What can we learn for computer vision? IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1847–1871. https://doi.org/10.1109/TPAMI.2012.272
Li, L., & Li, Q. (2023). Application of ultrathin asphalt overlay technology with 4.75-mm nominal maximum aggregate size (pp. 138–153). https://doi.org/10.2991/978-94-6463-336-8_16
Lin, S. L. (2023). Research on tire crack detection using image deep learning method. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-35227-z
Nur, A., Thohari, A., & Boy Hertantyo, G. (2018a). Implementasi Convolutional Neural Network untuk Klasifikasi Pembalap MotoGP Berbasis GPU.
Rodríguez, J. P., Fernández-Gracia, J., Thums, M., Hindell, M. A., Sequeira, A. M. M., Meekan, M. G., Costa, D. P., Guinet, C., Harcourt, R. G., McMahon, C. R., Muelbert, M., Duarte, C. M., & Eguíluz, V. M. (2017). Big data analyses reveal patterns and drivers of the movements of southern elephant seals. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-00165-0
Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. In Neural Networks (Vol. 61, pp. 85–117). Elsevier Ltd. https://doi.org/10.1016/j.neunet.2014.09.003
Sebastian, R., & Juliane, C. (2023). Comparison of Data Mining Classification Algorithms for Stroke Disease Prediction Using the SMOTE Upsampling Method (Vol. 11, Issue 2). https://sippn.menpan.go.id/,
Setiawan, A. P., Hambani, S., & Aziz, A. J. (2024). Factors Influencing Motor Vehicle Tax Compliance in Sukabumi Regency. Jurnal Ilmiah Akuntansi Kesatuan, 12(5), 459–468. https://doi.org/10.37641/jiakes.v12i5.2782
Solomon, S., Portmann, R. W., Sanders, R. W., & Daniel, J. S. (1998). Absorption of solar radiation by water vapor, oxygen, and related collision pairs in the Earth’s atmosphere. Journal of Geophysical Research Atmospheres, 103(D4), 3847–3858. https://doi.org/10.1029/97JD03285
syahid, m. f. (n.d.). implementasi deep learning vgg16 dengan transfer learning pada deteksi penyakit tanaman singkong skripsi oleh.
Taiwo, O. A., Hassan, S. A., Mohsin, R. Bin, & Mahmud, N. (n.d.). Road Traffic Accidents Involvement among Commercial Taxi Drivers in Nigeria: Structural Equation Modelling Approach. https://ssrn.com/abstract=45600.
M. S. Rahman, "Deteksi Kerusakan Ban Menggunakan CNN dengan Arsitektur ResNet-34," Jurnal Sains dan Teknologi, vol. 7, no. 4, pp. 321-327, 2021. [Online]. Available: https://ejurnal.umri.ac.id/index.php/JST/article/download/6336/2694.
A. Setiawan, "Deteksi Kerusakan Ban dengan CNN," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 2, pp. 134-140, 2021. [Online]. Available: https://repository.upnjatim.ac.id/9676/37/18081010016.-cover.pdf.
R. Hartono, "Deteksi Kecacatan Ban dengan CNN," Jurnal Sistem dan Komputer, vol. 10, no. 3, pp. 210-217, 2020. [Online]. Available: https://repository.telkomuniversity.ac.id/pustaka/files/186727/jurnal_eproc/sistem-deteksi-kecacatan-ban-dengan-convolutionalneural-network.pdf.
Listyalina, L., Buyung, I., Munir, A. Q., Mustiadi, I., & Dharmawan, D. A. (2022). Conv-Tire: Tire Feasibility Assessment using Convolutional Neural Networks Conv-Tire: Asesmen Kelayakan Ban berbasis Convolutional Neural Network. Jurnal Informatika Dan Teknologi Informasi, 19(3), 323–336. https://doi.org/10.31515/telematika.v19i3.7697
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Shofa Shofiah Hilabi, Ahmad Fauzi, Savina
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright © by the authors. Articles published in Applied Information System and Management (AISM) are open access and distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), https://creativecommons.org/licenses/by-sa/4.0/.
Authors retain copyright of their work without restriction and grant AISM a non-exclusive right of first publication. Under this license, anyone may share and adapt the article for any purpose, including commercially, provided that appropriate credit is given to the authors and to the first publication in AISM, a link to the license is provided, any changes are indicated, and adapted works are distributed under the same license.
Authors may deposit any version of their article, including the submitted manuscript, the accepted manuscript, and the published version, in institutional or subject repositories and on personal websites at any time, without embargo, with a full citation and a link to the DOI once published. Material from third parties that is not covered by the CC BY-SA 4.0 license is marked in the article.
See the Copyright and License policy: https://journal.uinjkt.ac.id/index.php/aism/CopyrightNotice
