• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.113-118
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• 2022

Motivated by people highlighting important phrases while reading or taking notes we propose a neural network training method by highlighting defective pixel areas to classify effectively defect types of images with complex background textures. To verify our proposed method we apply it to the problem of classifying the defect types of tire band fabric images that are too difficult to classify. In addition we propose a backlight highlighting technique which is tailored to the tire band fabric images. Backlight highlighting images can be generated by using both the GradCAM and simple image processing. In our experiment we demonstrated that the proposed highlighting method outperforms the traditional method in the view points of both classification accuracy and training speed. It achieved up to 13.4% accuracy improvement compared to the conventional method. We also showed that the backlight highlighting technique tailored for highlighting tire band fabric images is superior to a contour highlighting technique in terms of accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.849-852
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• 2021

This paper presents a vision based tire mold inspection system where mold defects are inspected and the sizes of specific parts of the mold are measured. There are a lot of challenging issues as letters and pictures of intaglio are engraved on a bright surface of the tire mold. To solve the issues, we carefully selected a line-scan camera and a line light. In addition, we used PLC to control the mechanical parts. The developed system provides inspection of misspelled and deformed letters as well as a variety of the functions such as size measurement of engraved regions and inspection report file creation.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.298-303
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• 2003

In recent years, shearogrpahy has significantly improved capabilities in the areas of unbond and separation detection in tires. Although shearography has many advantages for qualitative evaluation, the technique remains the problem of quantitative analysis of inside defects, because shearography needs several effective factors including the amount of shearing, shearing direction and induced load, which exist as barrier for the quantitative analysis of inside defects. Since the factors are highly dependent on inspectors skill and also affect the in-situ workability. The factors were optimized and the size of cracks inside of pipeline and tire has been quantitatively determined.

• Proceedings of the KIEE Conference
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• 2004.11d
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• pp.53-55
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• 2004

부분방전 검출을 이용한 XLPE 케이블 진단은 중간 및 종단 접속재의 계면에 존재하는 결합을 검출할 수 있는 가장 효과적인 방법으로 제시되고 있지만 현장의 큰 노이즈로 인하여 신뢰성 있는 진단이 쉽지 않다. 하지만 국내에서 많은 연구가 이루어진 송전케이블 진단 기술을 바탕으로 배전케이블 진단에 적용하여 종단접속부에서 발생된 부분방전을 성공적으로 검출하였고 해체 조사를 통하여 결함을 검출하였다. 본 논문은 참고문헌[1]에 발표한 논문에 연결되는 논문으로 전편은 2004년 2월 부분방전 검출사례를 보고한 논문이고 본 논문에서는 8월 계획정전을 통하여 문제된 접속함을 교체한 후 해체조사를 통하여 결함을 검출한 사례보고이다.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.7-12
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• 2018

In today's rapidly growing contemporary society, there is a tendency for demand to automate production processes by utilizing the vision system. In general, image recognition is mainly concerned with embossed characters such as license plates, and research on recognition of intaglio characters is very limited. Especially, intaglio characters, which are marked on rubber related products such as tire surfaces, have difficulty in recognizing characters or numbers through image because the difference in brightness with surrounding is not so large. In this paper, we propose a system to improve the recognition rate of characters marked on intaglio rubber products such as tire surfaces. Also, it can be applied flexibly according to the lighting environment. Through the proposed system, production and inventory management and defect detection can be processed quickly by applying to the production process of tire and rubber products.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.205-211
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• 2003

Acoustic emission (AE) technique has been applied to not only mechanical property testing but also on-line monitoring of the el)tire structure or a limit zone only. Although several AE devices have already been developed for the on-line monitoring, the price of these systems is very high and it is difficult for the field to apply yet. In this study, wc developed a specially designed PC-based source location system using the A/D board. The source location technique is very important to identify the source, such as crack, leak detection. However, since the AE waveforms obtained from transducers are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analyses of the transient waveform. Wavelet Transform (WT) is a powerful tool for processing transient signals with temporally varying spectra that helps to resolve high and low frequency transients components effectively In this study, the analyses of the AE signals are presented by employing the WT analyses. AE results are compared the PC-based source location system using A/D board with the commercial AE system.