• 제목/요약/키워드: Defect Segmentation

검색결과 35건 처리시간 0.023초

TFT-LCD영상에서 결함 가능성에 따른 순차적 결함영역 분할 (Sequential Defect Region Segmentation according to Defect Possibility in TFT-LCD Image)

  • 장충환;이승민;박길흠
    • 한국멀티미디어학회논문지
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    • 제23권5호
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    • pp.633-640
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    • 2020
  • Defect region segmentation of TFT-LCD images is performed by combining defect pixels detected by a defect detection method into defect region, or by using morphological operations to segment defect region. Therefore, the result of segmentation of the defect region is highly dependent on the defect detection result. In this paper, we propose a method which segments defect regions sequentially according to the possibility of being included in defect regions in TFT-LCD images. The proposed method repeats the process of detecting a seed using the median value and the median absolute deviation of the image, and segments the defect region using the seeded region growing method. We confirmed the superiority of the proposed method to segment defect regions using pseudo-images and real TFT-LCD images.

Data Segmentation for a Better Prediction of Quality in a Multi-stage Process

  • Kim, Eung-Gu;Lee, Hye-Seon;Jun, Chi-Hyuek
    • Journal of the Korean Data and Information Science Society
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    • 제19권2호
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    • pp.609-620
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    • 2008
  • There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

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직물 이미지 결함 탐지를 위한 딥러닝 기술 연구: 트랜스포머 기반 이미지 세그멘테이션 모델 실험 (Deep Learning Models for Fabric Image Defect Detection: Experiments with Transformer-based Image Segmentation Models)

  • 이현상;하성호;오세환
    • 한국정보시스템학회지:정보시스템연구
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    • 제32권4호
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    • pp.149-162
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    • 2023
  • Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network

  • Kim, Hyeonho;Lee, Suchul;Han, Seokmin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제14권12호
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    • pp.4763-4775
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    • 2020
  • This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

Accurate Detection of a Defective Area by Adopting a Divide and Conquer Strategy in Infrared Thermal Imaging Measurement

  • Jiangfei, Wang;Lihua, Yuan;Zhengguang, Zhu;Mingyuan, Yuan
    • Journal of the Korean Physical Society
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    • 제73권11호
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    • pp.1644-1649
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    • 2018
  • Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

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

  • 시종욱;김성영
    • 한국정보전자통신기술학회논문지
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    • 제16권6호
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    • pp.369-377
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    • 2023
  • 산업 제조 분야에서 품질 관리는 불량률을 최소화하는 핵심 요소로, 미흡한 관리는 추가적인 비용 발생과 생산 지연을 야기할 수 있다. 본 연구는 제조품의 텍스쳐 결함 감지의 중요성을 중심으로, 보다 정밀한 결함 감지 방법을 제시한다. DFR(Deep Feature Reconstruction) 모델은 특징맵의 조합 및 재구성을 통한 접근법을 채택하였지만, 그 방식에는 한계가 있었다. 이에 따라, 우리는 제한점을 극복하기 위해 통계적 방법론을 활용한 새로운 손실 함수와 스킵 연결구조를 통합하고 파라미터 튜닝을 진행하였다. 이 개선된 모델을 MVTec-AD 데이터세트의 텍스쳐 카테고리에 적용한 결과, 기존 방식보다 2.3% 높은 결함 분할 AUC를 기록하였고, 전체적인 결함 감지 성능도 향상되었다. 이 결과는 제안하는 방법이 특징맵 조합의 재건축을 통한 결함 탐지에 있어서 중요한 기여함을 입증한다.

영상 세그멘테이션 및 템플리트 매칭 기술을 응용한 필름 결함 검출 시스템 (A Film-Defect Inspection System Using Image Segmentation and Template Matching Techniques)

  • 윤영근;이석룡;박호현;정진완;김상희
    • 한국정보과학회논문지:데이타베이스
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    • 제34권2호
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    • pp.99-108
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    • 2007
  • 본 논문에서는 TFT-LCD에 사용되는 편광 필름(polarized film)의 제작 과정 중 최종 단계에서 수행되는 필름의 결함 검출 및 결함 유형을 판정하기 위한 필름 결함 검출 시스템(Film Defect Inspection System: FDIS)을 설계하고 이를 구현하였다. 제안한 시스템은 영상 세그멘테이션 기법을 이용하여 편광 필름 영상으로부터 결함을 검출하였고, 검출된 결함의 영상을 분석하여 결함 유형을 판정할 수 있도록 설계되었다. 결함 유형의 판정은 결함 영역의 형태적 특성 및 질감(texture) 등의 특징을 추출하여 템플리트(template) 데이타베이스에 저장된 기준(reference) 결함 영상과 비교함으로써 수행된다. FDIS를 이용한 실험 결과, 테스트 영상에서 모든 결함 영역을 빠른 시간 안에 (평균 0.64초), 정확히 검출하였으며(Precision 1.0, Recall 1.0), 결함 유형을 판정하는 실험에서도 평균 Precision 0.96, Recall 0.95로 정확도가 매우 높은 것을 관찰할 수 있었다. 또한 회전 변형을 적용한 경우의 결함 유형 검출 실험에서도 평균 Precision 0.95, Recall 0.89로 제안한 기법이 회전 변환에 대하여 견고함을 보여 주었다.

TFT-LCD 패널 검사를 위한 지역적 분별에 기반한 결함 영역 분할 알고리즘 (Segmentation of Defective Regions based on Logical Discernment and Multiple Windows for Inspection of TFT-LCD Panels)

  • 정건희;정창도;윤병주;이준재;박길흠
    • 한국멀티미디어학회논문지
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    • 제15권2호
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    • pp.204-214
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    • 2012
  • 본 논문은 비전장비의 결함 검사 시스템을 위한 불균일한 휘도분포를 가지는 TFT-LCD 영상에서 결함 영역을 분할하는 방법을 다룬다. 불균일한 휘도분포 때문에 결함의 영역을 찾기 어려우며 이를 위해 많은 방법들이 제안되었다. Kamel과 Zhoa는 문자 및 그래픽의 분할을 위해 논리적 단계화 방법을 제안하였고, 이 방법은 공간상에서 수행되어지는 지역적 분할 방법으로 불균일한 분포 상에서도 문자가 잘 분할되는 장점이 있다. TFT-LCD의 저해상도 영상도 배경의 분포가 불균일하여 본 논문에서는 Kamel과 Zhoa의 방법을 답습하여 새로운 결함 영역 분할 방법을 제안한다. 제안한 방법은 결함주위에 발생하는 과검출(Ghost object)이 적은 장점이 있으며 제안 방법의 성능을 증명하기위해 실제 결함이 존재하는 TFT-LCD 영상을 이용하여 실험하고, 주파수상에서 많이 사용되는 FFT의 밴드패스 필터를 이용한 분할 방법과 비교하였다.

휴대폰 키버튼 불량 검사 시스템 (Keypad Button Defect Inspection System of Cellphone)

  • 이준재
    • 한국멀티미디어학회논문지
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    • 제13권2호
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    • pp.196-204
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    • 2010
  • 본 논문에서는 핸드폰 조립 전의 키패드의 개별버튼에 불량 검사방법을 제안한다. 제안한 알고리즘은 키패드의 영역 분할, 폰트 영역 분할, 이동 및 회전의 처리과정을 통해, 동일 색상의 검사 및 등급 분류, 폰트 검사, 스크래치 검사로 이루어져 있다. 특히, 본 논문에서 제안한 영역분할 방법은 기존의 단순 문턱치를 기반으로 키패드의 경사나 곡면 모양에 기인한 휘도 변화에 대응하기 위해, 패드 영역만을 B-spline으로 근사화하여, 각 화소마다 다른 표면 문턱치를 적용하는 방법을 제시한다. 또한, 키패드의 회전 정보를 고유치 및 고유벡터를 사용하여 매우 빠르고 효율적으로 구하는 방법을 제시한다. 실험결과 제안한 방법은 실제 인라인 공정상에 적용하여 실험결과 우수한 성능을 보여준다.

Current Trend and Direction of Deep Learning Method to Railroad Defect Detection and Inspection

  • Han, Seokmin
    • International Journal of Internet, Broadcasting and Communication
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    • 제14권3호
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    • pp.149-154
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    • 2022
  • In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.