• 전기전자학회논문지
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• 제18권2호
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1099-1101
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• 2003

Change detection technology, which discovers the change information on the surface of the earth by comparing and analyzing multi-temporal satellite images, can be usefully applied to the various fields, such as environmental inspection, urban planning, forest policy, updating of geographical information and the military usage. In this paper, we introduce a change detection system that can extract and analyze change elements from high-resolution satellite imagery as well as low- or middle-resolution satellite imagery. The developed system provides not only 7 pixelbased methods that can be used to detect change from low- or middle-resolution satellite images but also a float window concept that can be used in manual change detection from highresolution satellite images. This system enables fast access to the very large image, because it is constituted by OGC grid coverage components. Also new change detection algorithms can be easily added into this system if once they are made into grid coverage components.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• 산업식품공학
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• 제21권3호
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• pp.191-200
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• 2017

There have been great efforts to develop a rapid and sensitive detection method to monitor the presence of pathogenic bacteria in food. While a number of methods have been reported for bacterial detection with a detection limit to a single digit, most of them are suitable only for the bacteria in pure culture or buffered solution. On the other hand, foods are composed of highly complicated matrices containing carbohydrate, fat, protein, fibers, and many other components whose composition varies from one food to the other. Furthermore, many components in food interfere with the downstream detection process, which significantly affect the sensitivity and selectivity of the detection. Therefore, isolating and concentrating the target pathogenic bacteria from food matrices are of importance to enhance the detection power of the system. The present review provides an introduction to the representative sample preparation strategies to isolate target pathogenic bacteria from food sample. We further describe the nucleic acid-based detection methods, such as PCR, real-time PCR, NASBA, RCA, LCR, and LAMP. Nucleic acid-based methods are by far the most sensitive and effective for the detection of a low number of target pathogens whose performance is greatly improved by combining with the sample preparation methods.

• Earthquakes and Structures
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• 제22권1호
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• pp.1-13
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• 2022

When a building suffers damages under moderate to severe loading condition, its physical properties such as damping and stiffness parameters will change. There are different practical methods besides various numerical procedures that have successfully detected a range of these changes. Almost all the previous proposed methods used to work with translational components of mode shapes, probably because extracting these components is more common in vibrational tests. This study set out to investigate the influence of using both rotational and translational components of mode shapes, in detecting damages in 3-D steel structures elements. Three different sets of measured components of mode shapes are examined: translational, rotational, and also rotational/translational components in all joints. In order to validate our assumptions two different steel frames with three damage scenarios are considered. An iterative model updating program is developed in the MATLAB software that uses the OpenSees as its finite element analysis engine. Extensive analysis shows that employing rotational components results in more precise prediction of damage location and its intensity. Since measuring rotational components of mode shapes still is not very convenient, modal dynamic expansion technique is applied to generate rotational components from measured translational ones. The findings indicated that the developed model updating program is really efficient in damage detection even with generated data and considering noise effects. Moreover, methods which use rotational components of mode shapes can predict damage's location and its intensity more precisely than the ones which only work with translational data.

• 정보처리학회논문지B
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• 제14B권4호
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• pp.255-262
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• 2007

본 연구는 칼라 이미지에서 인물의 얼굴 영역을 검출하는 개선된 색상 기반 방식을 제안한다. 제안 방법은 RGB, $YC_bC_r$, YIQ의 세 가지 색상 모델을 조합, 각각 휘도와 색도 성분 조합 히스토그램을 구축하고 구축된 색상 조합 히스토그램을 역전파방식의 신경망에 입력한 후 학습단계의 반본 과정에 Levenberg-Marquadt 알고리즘을 적용한다. 제안 방법은 신경망 학습과정에 Levenberg-Marquadt 알고리즘을 적용하여 얼굴 검출에 가장 많이 사용되는 방법 중 하나인 역전파 신경망이 지역 최소값에 봉착하는 문제점을 해결함으로써 검출 오류율을 낮추는데 기여한다. 또한 색상 조합 히스토그램을 사용한 새로운 색상 조합 기반의 얼굴 영역 검출 방법은 빛의 영향에 강건하도록 휘도 성분을 분리하고 색도 성분을 강조하여 단일 색상 히스토그램보다 신경망에 더 신뢰성 있는 값을 입력함으로써 단일 색상 공간을 사용했을 때보다 높은 얼굴 검출율을 보인다. 실험 결과는 제안 방식이 얼굴 영역 검출 개선에 효과적이며 빛의 변화에 강건함을 보여준다.

• 로봇학회논문지
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• 제3권3호
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• pp.165-175
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• 2008

For an advanced intelligent service, the need of HRI technology has recently been increasing and the technology has been also improved. However, HRI components have been evaluated under stable and controlled laboratory environments and there are no evaluation results of performance in real environments. Therefore, robot service providers and users have not been getting sufficient information on the level of current HRI technology. In this paper, we provide the evaluation results of the performance of the HRI components on the robot platforms providing actual services in pilot service sites. For the evaluation, we select face detection component, speaker gender classification component and sound localization component as representative HRI components closing to the commercialization. The goal of this paper is to provide valuable information and reference performance on appling the HRI components to real robot environments.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.988-999
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• 2021

AOI (Automatic Optical Inspection) of PCB (Printed Circuit Board) is a very important step to guarantee the product performance. The process of registering components called teaching mode is first perform, and AOI is then carried out in a testing mode that checks defects, such as recognizing and comparing the component mounted on the PCB to the stored components. Since most of registration of the components on the PCB is done manually, it takes a lot of time and there are many problems caused by mistakes or misjudgement. In this paper, A components classifier is proposed using YOLO (You Only Look Once) v2's object detection model that can automatically register components in teaching modes to reduce dramatically time and mistakes. The network of YOLO is modified to classify small objects, and the number of anchor boxes was increased from 9 to 15 to classify various types and sizes. Experimental results show that the proposed method has a good performance with 99.86% accuracy.

• 센서학회지
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• 제28권3호
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• pp.157-163
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• 2019

The LiDAR sensor, which is widely regarded as one of the most important sensors, has recently undergone active commercialization owing to the significant growth in the production of ADAS and autonomous vehicle components. The LiDAR sensor technology involves radiating a laser beam at a particular angle and acquiring a three-dimensional image by measuring the lapsed time of the laser beam that has returned after being reflected. The LiDAR sensor has been incorporated and utilized in various devices such as drones and robots. This study focuses on object detection and recognition by employing sensor fusion. Object detection and recognition can be executed as a single function by incorporating sensors capable of recognition, such as image sensors, optical sensors, and propagation sensors. However, a single sensor has limitations with respect to object detection and recognition, and such limitations can be overcome by employing multiple sensors. In this paper, the performance of an eight-channel scanning LiDAR was evaluated and an object detection algorithm based on it was implemented. Furthermore, object detection characteristics during daytime and nighttime in a real road environment were verified. Obtained experimental results corroborate that an excellent detection performance of 92.87% can be achieved.

• 대한기계학회논문집A
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• 제37권9호
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• pp.1175-1182
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• 2013

위상잠금모드가 적용된 적외선열화상 시험법은 열전도도가 큰 재료에서 결함의 검출능력을 향상시키기 위해 개발되었으며, 기존의 적외선열화상 기법에 비해 우수한 결함 검출능력을 보이는 것으로 알려져 있다. 따라서, 본 연구에서는 원전 배관 감육결함 검출에서 위상잠금모드 기법의 적용 효과를 살펴보기 위해서, 감육결함이 가공된 배관 시편을 대상으로 위상잠금모드를 적용하여 적외선열화상 시험을 수행하였다. 시험 결과로부터 감육결함에 대한 위상이미지를 얻고, 이것을 기존의 적외선열화상시험법으로 구한 열화상이미지와 비교하였다. 비교 결과, 위상잠금모드의 적용이 감육결함에 대한 형상 결정 능력을 향상시키는 것을 확인하였다. 이러한 개선 효과는 폭과 길이가 작거나 경계가 경사진 감육결함에서 뚜렷하였다. 그러나, 깊이가 얕은 감육결함의 검출능력은 크게 향상되지 않았다.