Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

Latifah Listyalina, Irawadi Buyung, A. Munir, Ikhwan Mustiadi, Dhimas Arief Dharmawan
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引用次数: 0

Abstract

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.
卷积-轮胎:使用卷积神经网络进行轮胎状态评估
目的:设计一种基于卷积神经网络的轮胎质量自动评估算法。设计/方法论/方法:提出的算法是通过以下几个阶段构建的。在第一阶段,获取轮胎图像作为设计算法的输入。进一步,将采集到的图像分为训练集和测试集两组。训练集包含几个卷积神经网络(CNN)架构(如ResNet-50, MobileNetV2, Inception V3和DenseNet-121)的训练阶段使用的轮胎图像。训练阶段分多个epoch进行,在每个epoch计算交叉熵损失函数,该函数表示CNN架构对轮胎图像进行分类的性能。因此,训练阶段需要一个标签或参考,以显示每个图像中显示的轮胎的可行性。发现/结果:在测试阶段,使用训练好的CNN架构对测试集中的轮胎图像进行分类。测试集的分类性能也用交叉熵损失函数值表示。此外,还计算了准确率值,该值显示了成功分类正确的轮胎图像数量占测试集中轮胎图像总数的百分比,即DenseNet-121模型的准确率最高,为92.62%。原创性/价值/技术水平:由于我们的算法达到了很高的准确率,这项工作可以作为其他研究人员的参考,特别是对他们未来开发的轮胎质量分类方法进行基准测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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7
审稿时长
24 weeks
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