• Title/Summary/Keyword: Defect detection

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Implementation of Paper Cutting Defect Detection System Based on Local Binary Pattern Analysis (국부 이진 패턴 분석에 기초한 지절 결함 검출 시스템 구현)

  • Kim, Jin-Soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.9
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    • pp.2145-2152
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    • 2013
  • Paper manufacturing industries have huge facilities with automatic equipments. Especially, in order to improve the efficiency of the paper manufacturing processes, it is necessary to detect the paper cutting defect effectively and to classify the causes correctly. In this paper, we review the problems of web monitoring system and web inspection system that have been traditionally used in industries for defect detection. Then we propose a novel paper cutting defect detection method based on the local binary pattern analysis and its implementation to mitigate the practical problems in industry environment. The proposed algorithm classifies the defects into edge-type and region-type and then it is shown that the proposed system works stably on the real paper cutting defect detection system.

Development of Automated Surface Inspection System using the Computer V (컴퓨터 비젼을 이용한 표면결함검사장치 개발)

  • Lee, Jong-Hak;Jung, Jin-Yang
    • Proceedings of the KIEE Conference
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    • 1999.07b
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    • pp.668-670
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    • 1999
  • We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.

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Development of a Steel Plate Surface Defect Detection System Based on Small Data Deep Learning (소량 데이터 딥러닝 기반 강판 표면 결함 검출 시스템 개발)

  • Gaybulayev, Abdulaziz;Lee, Na-Hyeon;Lee, Ki-Hwan;Kim, Tae-Hyong
    • IEMEK Journal of Embedded Systems and Applications
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    • v.17 no.3
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    • pp.129-138
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    • 2022
  • Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.

Application of YOLOv5 Neural Network Based on Improved Attention Mechanism in Recognition of Thangka Image Defects

  • Fan, Yao;Li, Yubo;Shi, Yingnan;Wang, Shuaishuai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.1
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    • pp.245-265
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    • 2022
  • In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

Detection of Defect Patterns on Wafer Bin Map Using Fully Convolutional Data Description (FCDD) (FCDD 기반 웨이퍼 빈 맵 상의 결함패턴 탐지)

  • Seung-Jun Jang;Suk Joo Bae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.46 no.2
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    • pp.1-12
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    • 2023
  • To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Enhanced Deep Feature Reconstruction : Texture Defect Detection and Segmentation through Preservation of Multi-scale Features (개선된 Deep Feature Reconstruction : 다중 스케일 특징의 보존을 통한 텍스쳐 결함 감지 및 분할)

  • Jongwook Si;Sungyoung Kim
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.6
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    • pp.369-377
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    • 2023
  • In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.

Performance Comparison of Scaffold Defect Detection Model by Parameters (파라미터에 따른 인공지지체 불량 탐지 모델의 성능 비교)

  • Song Yeon Lee;Yong Jeong Huh
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.1
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    • pp.54-58
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    • 2023
  • In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

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Defect Detection Method using Human Visual System and MMTF (MMTF와 인간지각 특성을 이용한 결함성분 추출기법)

  • Huh, Kyung-Moo;Joo, Young-Bok
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.12
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    • pp.1094-1098
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    • 2013
  • AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. Defect detection is not an easy process because of noises from various sources and optical distortion. In this paper the acquired images from a TFT panel are enhanced with the adoption of an HVS (Human Visual System). A human visual system is more sensitive on the defect area than the illumination components because it has greater sensitivity to variations of intensity. In this paper we modified an MTF (Modulation Transfer Function) in the Wavelet domain and utilized the characteristics of an HVS. The proposed algorithm flattens the inner illumination components while preserving the defect information intact.

Imaging of a Defect in Thin Plates Using the Time Reversal of Single Mode Lamb Wave: Simulation

  • Jeong, Hyun-Jo;Lee, Jung-Sik;Bae, Sung-Min;Lee, Hyun-Ki
    • Journal of the Korean Society for Nondestructive Testing
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    • v.30 no.3
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    • pp.261-270
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    • 2010
  • This paper presents an analytical investigation for a baseline-free imaging of a defect in plate-like structures using the time-reversal of Lamb waves. We first consider the flexural wave (A0 mode) propagation in a plate containing a defect, and reception and time reversal process of the output signal at the receiver. The received output signal is then composed of two parts: a directly propagated wave and a scattered wave from the defect. The time reversal of these waves recovers the original input signal, and produces two additional side bands that contain the time-of-flight information on the defect location. One of the side band signals is then extracted as a pure defect signal. A defect localization image is then constructed from a beamforming technique based on the time-frequency analysis of the side band signal for each transducer pair in a network of sensors. The simulation results show that the proposed scheme enables the accurate, baseline-free detection of a defect, so that experimental studies are needed to verify the proposed method and to be applied to real structure.

Development of real-time defect detection technology for water distribution and sewerage networks (시나리오 기반 상·하수도 관로의 실시간 결함검출 기술 개발)

  • Park, Dong, Chae;Choi, Young Hwan
    • Journal of Korea Water Resources Association
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    • v.55 no.spc1
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    • pp.1177-1185
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    • 2022
  • The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.