Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

• Published in: Telematika (Yogyakarta, Indonesia) Vol. 19; no. 3; p. 323
• Main Authors: Listyalina, Latifah, Buyung, Irawadi, Munir, Agus Qomaruddin, Mustiadi, Ikhwan, Dharmawan, Dhimas Arief
• Format: Journal Article
• Language: English
• Published: 31.10.2022
• ISSN: 1829-667X; 2460-9021
• DOI: 10.31315/telematika.v19i3.7697

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.