Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

Latifah Listyalina, Irawadi Buyung, Agus Qomaruddin Munir, Ikhwan Mustiadi, Dhimas Arief Dharmawan


Purpose: In this study, the authors designed an algorithm based on convolutional neural networks that can automatically assess tire quality.

Design/methodology/approach: The proposed algorithm is built through several stages as follows. In the first stage, the tire images, which are the input of the designed algorithm, are acquired. Further, the acquired images are divided into two sets, namely training and testing sets. The training set contains tire images used in the training phase of several convolutional neural networks (CNN) architectures such as ResNet-50, MobileNetV2, Inception V3, and DenseNet-121. The training phase is carried out in a number of epochs, and at each epoch, the cross entropy loss function will be calculated which expresses the performance of the CNN architecture in classifying tire images. For this reason, the training stage requires a label or reference that shows the feasibility of the tires displayed in each image.

Findings/result: In the testing phase, trained CNN architectures are used to classify tire images from the test set. Classification performance in the test set is also expressed in terms of cross-entropy loss function value. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, namely the DenseNet-121 model has the best accuracy of 92.62%.

Originality/value/state of the art: Given the high accuracy achieved by our algorithm, this work can be used as a reference by other researchers, specifically to benchmark their tire quality classification methods developed in the future.


convolutional neural network; image; tire; tire quality

Full Text:



S. Erdogan, “Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey,” Journal of Safety Research, vol. 40, no. 5, pp. 341–351, 2009.

X. Cui, Y. Liu, and C. Wang, “Defect automatic detection for tire X-ray images using inverse transformation of principal component residual,” in 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), 2016, pp. 1–8.

Y. Xiang, C. Zhang, and Q. Guo, “A dictionary-based method for tire defect detection,” in 2014 IEEE International Conference on Information and Automation (ICIA), 2014, pp. 519–523.

Q. Zhu and X. Ai, “The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning,” in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018, pp. 138–142.

Y. Li, B. Fan, W. Zhang, and Z. Jiang, “TireNet : A high recall rate method for practical application of tire defect type classification,” Future Generation Computer Systems, vol. 125, pp. 1–9, 2021.

R. Wang, Q. Guo, S. Lu, and C. Zhang, “Tire Defect Detection Using Fully Convolutional Network,” IEEE Access, vol. 7, pp. 43502–43510, 2019.

C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 07-12-June, pp. 1–9.

Y. Jusman, I. M. Firdiantika, D. A. Dharmawan, and K. Purwanto, “Performance of multi layer perceptron and deep neural networks in skin cancer classification,” in 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), 2021, pp. 534–538.

L. Listyalina and I. Mustiadi, “Accurate and Low-cost Fingerprint Classification via Transfer Learning,” in 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, 2019, pp. 27–32.

R. B. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” CoRR, vol. abs/1311.2, 2013.

J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” CoRR, vol. abs/1506.0, 2015.

L. Listyalina, I. Mustiadi, and D. A. Dharmawan, “Joint Dice and Intersection over Union Losses for Deep Optical Disc Segmentation,” in 2020 3rd International Conference on Biomedical Engineering (IBIOMED), 2020, pp. 49–54.

D. A. Dharmawan, D. Li, B. P. Ng, and S. Rahardja, “A New Hybrid Algorithm for Retinal Vessels Segmentation on Fundus Images,” IEEE Access, vol. 7, pp. 41885–41896, 2019.

D. Li, D. A. Dharmawan, B. P. Ng, and S. Rahardja, “Residual U-Net for Retinal Vessel Segmentation,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 1425–1429.

G. Huang, Z. Liu, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” CoRR, vol. abs/1608.0, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision.” arXiv, 2015.




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright of :
TELEMATIKA: Jurnal Informatika dan Teknologi Informasi
ISSN 1829-667X (print); ISSN 2460-9021 (online)

Dipublikasi oleh
Jurusan Teknik Informatika, UPN Veteran Yogyakarta
Jl. Babarsari 2 Yogyakarta 55281 (Kampus Unit II)
Telp: +62 274 485786


Jurnal Telematika sudah diindeks oleh beberapa lembaga berikut:





Status Kunjungan Jurnal Telematika