Defect Classification on Automobile Tire Inner Surfaces using Convolutional Neural Networks

Tada, Hirotaro; Sugiura, Akihiko

doi:10.1109/iccubea.2017.8463768

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Both surface and inner defect detection methods based on deep learning also emerged. Tada et al [11] applied CNN to realize the classification of tire inner surface defects using a two-step classifier. Cui et al [12] proposed a multi-contrast-CNN to classify various defects in tire radiographic images.…”

Section: Introductionmentioning

confidence: 99%

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

Sun

Liu

Zhai

et al. 2023

Meas. Sci. Technol.

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Efficient and accurate visual inspection of products is of great significance for intelligent manufacturing. In this paper, a visual inspection framework based on a lightweight Transformer is proposed for the pixel-level inspection of tire defects. A DP-Transformer feature encoder was proposed based on a hybrid architecture of convolutional neural network (CNN) and Transformer, which is used to learn the local and global relationship of defect features. Moreover, multi-scale fusion Transformer and spatial cross Transformer were proposed, based on which a feature decoder was built. In the decoder, the multi-scale fusion Transformer provides valuable spatial information for spatial cross Transformer, such that different levels of feature maps can refine the dependency of pixels through spatial cross Transformer. The proposed method is tested on the tire radiographic image dataset. Experimental results show that the proposed method reaches a detection accuracy of 98.57% and mIoU of 85.56%. Moreover, the method achieves the balance between accuracy and efficiency with satisfactory characterization ability for the defect geometric shape, and provides theoretical support for the future industrial deployment of transformer.

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Section: Introductionmentioning

confidence: 99%

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

Sun

Liu

Zhai

et al. 2023

Meas. Sci. Technol.

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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%

“…Defect detection techniques based on deep learning have made great progress by virtue of their dramatically increased performance in feature extraction and representation. Deep learning-based tire defect detection methods can be divided into the following three main categories, namely image classification [26][27][28][29], image segmentation [30][31][32] and object detection [33][34][35]. Tada and Sugiura applied convolutional neural network (CNN) to tire inner surface images for classification [26], which showed effectiveness and robustness in classifying objects with large variations in shape, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning-based tire defect detection methods can be divided into the following three main categories, namely image classification [26][27][28][29], image segmentation [30][31][32] and object detection [33][34][35]. Tada and Sugiura applied convolutional neural network (CNN) to tire inner surface images for classification [26], which showed effectiveness and robustness in classifying objects with large variations in shape, etc. By replacing the convolutional layers in a convolutional network with sparse coding, Zheng et al [27] achieved unsupervised classification of tire defects.…”

Section: Introductionmentioning

confidence: 99%

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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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“…14 The experimental results show that the method meets the requirements for robustness and accuracy of metal defect detection. In one study, 15 a multi-step CNN was proposed to classify defects on the inner surface of tires, and the accuracy was improved by 17%. A multi-class defect detection model for electrical equipment was proposed in another study based on a fast region-based R-CNN model.…”

Section: Introductionmentioning

confidence: 99%

Textile Defect Classification Based on Convolutional Neural Network and SVM

Qiu

Cui

et al. 2021

AATCC Journal of Research

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Textile defect detection and classification is an important part of the textile production process, however, to detect accurately and efficiently is still difficult. In this study, we present an effective formulation for textile defect detection. Unlike traditional textile detecting methods, a conventional neural network (CNN) support vector machine (SVM) is designed to extract the depth features of textile images and to classify defects. The effectiveness of textile feature extraction is improved by optimizing Alexnet to obtain a CNN with better feature extraction and less computation. Textile defect classification is more effective using SVM instead of Softmax. Experimental results demonstrated that the improved Alexnet-SVM gave 99% accuracy in defect classification using the TILDA database. This accuracy was 5% greater than Alexnet and GoogleNet.

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Defect Classification on Automobile Tire Inner Surfaces using Convolutional Neural Networks

Cited by 8 publications

References 2 publications

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

MSANet: efficient detection of tire defects in radiographic images

Textile Defect Classification Based on Convolutional Neural Network and SVM

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