TireNet: A high recall rate method for practical application of tire defect type classification

Liu, Ying; Fan, Binbin; Zhang, Weiping; Jiang, Zhiqiang

doi:10.1016/j.future.2021.06.009

Cited by 26 publications

(17 citation statements)

References 9 publications

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“…The TireNet model, proposed in [1], is an end-to-end technique for practical use in x-ray image-based tire defect identification. It utilized the Siamese network as part of a downstream classifier to collect faulty features.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the annual return of defective tires at a significant rate of 7% leads to a substantial financial burden of $100 million in restitution. Despite the substantial allocation of resources to tire manufacturing, the industry continues to grapple with the task of guaranteeing the manufacture of superior products and minimizing the frequency of defective tire returns in order to reduce financial setbacks [1]. In order to reduce the quantity of tires being returned, it is imperative to carry out comprehensive quality checks, which encompass the identification of defects through the utilization of x-ray imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

“…One primary concern pertains to the existence of more than 200 unique designs and standards, leading to a considerable diversity of tire depictions. A further problem arises from the diverse range of defects observed in tire production, which exhibit distinct characteristics, encompassing over 20 different types of defects [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Saleh,

Metin ERTUNÇ

2024

Eng. Res. Express

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In the tire manufacturing industry, ensuring the quality of tires is of utmost importance because defective tires have the potential to fail explosively, particularly in high-speed driving situations such as races. To address this issue, thorough visual inspections after production are essential. Nevertheless, detecting defects in tires is a challenging task due to the various textures and structures they have. This paper introduces an Explainable Attention-based Fused Convolutional Neural Network (XAFCNN) model for tire defect detection. Special Attention Module (SAM) is used to prevent overfitting and improve local feature mapping, enabling the detection of small objects. The Grad-CAM heatmap method is utilized to offer significant visual indicators that enrich our comprehension of how the suggested model analyzes data and discerns relevant features within the tire samples. The model was trained on a dataset comprising 38,710 X-ray images of defective tires and 83,985 defect-free tire images, encompassing 15 defect types across 50 design patterns. The results demonstrate the model's exceptional performance, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. Notably, the model excels in identifying diverse tire defects and complex textures, making it highly effective in real-world scenarios. By providing both a substantial dataset and a high-performing model, this research advances automated tire defect detection, meeting the industry's imperative for accurate and reliable inspections, and ultimately enhancing human safety.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Saleh,

Metin ERTUNÇ

2024

Eng. Res. Express

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“…Pemanfaatan CNN untuk sttudi kelayakan ban juga dilakukan oleh [5]. Pada penelitian teresebut, metode end-to-end (TireNet) diusulkan untuk aplikasi praktis deteksi cacat ban otomatis dengan gambar sinar-X.…”

Section: Penelitian Terkaitunclassified

Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

et al. 2022

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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.

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“…In recent years, deep learning technology has been widely used in many fields, such as agricultural inspection [22], medical image processing [23][24][25] and defect detection [26][27][28][29][30][31][32][33][34][35]. Defect detection techniques based on deep learning have made great progress by virtue of their dramatically increased performance in feature extraction and representation.…”

Section: Introductionmentioning

confidence: 99%

MSANet: efficient detection of tire defects in radiographic images

Zhao¹,

Zheng²,

Sun³

et al. 2022

Meas. Sci. Technol.

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Visual inspection has been widely studied and applied in industrial fields. Previous work has investigated the use of established traditional machine learning and deep learning methods to perform automated defect detection for tires. However, intelligent tire defect online detection is still a challenging task due to the complex anisotropic texture background of tire radiographic image. In this paper, we propose an efficient tire defect online detection method named MSANet based on an improved lightweight Yolov4 tiny network. A novel multi-scale self-attention feature enhancement module (MSAM) is designed to extract feature map with rich multi-scale context information. An improved feature pyramid model, named MC-FPN is proposed which utilizes MSAM and convolutional block attention module (CBAM) to enhance the information representation of the feature pyramid. Ablation experiments are conducted to verify the effectiveness of the proposed modules. Comparative experimental results with the state-of-the-art methods validate the effectiveness and efficiency of the proposed method which can achieve a mAP of 96.96% and an average detection time of 30.81ms per image. The proposed method can meet the requirements of industrial online detection by virtue of its lower computational costs, and has good generalization ability in other visual inspection tasks.

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TireNet: A high recall rate method for practical application of tire defect type classification

Cited by 26 publications

References 9 publications

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks

MSANet: efficient detection of tire defects in radiographic images

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