• Journal of Korea Water Resources Association
• /
• v.55 no.spc1
• /
• pp.1177-1185
• /
• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.99-104
• /
• 2011

In this paper the approach of neural network was proposed which detects a variety of defects in the molded parts. In an attempt to improve the response of the system, It is designed to minimize the use of memory via LookUp table in software. The goal of these methods was to extract the features of samples in learning of neural networks, overcoming the algorithms of defects detection and classification. Through the learning of 500 sample patterns of molded parts, defects of 3% molded parts was detected and classified as the incorrect diameter parts. We expect that proposed approach is an effective alternative to save test time and cost for defect detection of a fine pattern within the molded parts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.211-213
• /
• 2019

This paper proposes an image detection method, which can detect images regardless of the location and the direction of an image, required for automatic inspection based on machine vision technologies. A cable harness is considered in this paper as an inspection object, and implementation results of a technology of being applicable to a real cable harness production process is presented.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.11a
• /
• pp.122-123
• /
• 2018

본 논문에서는 옥타브(sacle vector, octave)를 0, 시그마(sigma)는 1.6, 간격(intervals)은 3으로 설정하여 검출한 RobHess SIFT 특징들로 데이터 셋을 만들어 딥 러닝 모델인 VGG-16을 기반으로 SIFT 이미지 특징을 검출하는 방법을 제안한다. DIV2K 데이터 셋을 $33{\times}33$ 크기로 잘라서 데이터 셋을 구성하였고, 흑백 영상으로 판별하는 SIFT와는 달리 RGB 영상을 사용 하였다. 영상을 좌 우 반전, 밝기, 회전, 크기를 조절하여 원본 영상을 변형시켜 네트워크 학습 및 평가를 진행하였다. 네트워크는 영상의 가운데에 위치한 픽셀이 특징점인지 아닌지를 판별한다. 검증 데이터의 결과 98.207%의 정확도를 얻었다.

• Journal of Korea Multimedia Society
• /
• v.15 no.4
• /
• pp.439-448
• /
• 2012

TFT-LCD panel images have non-uniform brightness, noise signal and defect signal. It is hard to divide defect signal because of non-uniform brightness and noise signal, so various divide methods have being developed. In this paper, we suggest method to divide defective regions on TFT-LCD panel image by estimating a menas of two different size of windows, which is suggested by Eikvil et al., and using difference of them. But in this method, the size of detectable defects is restricted by the size of window, hence it has inefficient problem that the size of window have to increase to divide a large defect region. To solve this problem we suggest an algorithm which can divide various size of defects, by using Multi-scale and restrict a detectable size of defects in each scale. To prove an efficiency of suggested algorithm, we show that resulting images of real TFT-LCD panel images and an artificial image with various defects.

• Journal of the Korean Society for information Management
• /
• v.25 no.2
• /
• pp.49-68
• /
• 2008

In many information retrieval experiments, the data fusion techniques have been used to achieve higher effectiveness in comparison to the single evidence-based retrieval. However, there had not been many image retrieval studies using the data fusion techniques especially in combining retrieval results based on multiple retrieval methods. In this paper, we describe how the image retrieval effectiveness can be improved by combining two sets of the retrieval results using the Sobel operator-based edge detection and the Self Organizing Map(SOM) algorithms. We used the clip art images from a commercial collection to develop a test data set. The main advantage of using this type of the data set was the clear cut relevance judgment, which did not require any human intervention.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06c
• /
• pp.450-453
• /
• 2007

기존에 널리 이용되고 있는 일회용 용기가 가지고 있는 환경 오염 문제로 인하여 천연 고분자로 만들어진 생분해성 일회용 용기의 사용이 점점 증가하고 있다. 본 논문에서는 생분해성 용기 제작시 중요한 단계인 양품 및 불량품 판정을 위하여 비젼 시스템으로부터 획득된 용기 이미지로부터 용기 내부면 및 테두리에서 발생하는 결함을 검출하는 알고리즘을 제안한다. 구현한 시스템을 통한 실험 결과 제안한 알고리즘은 실시간 제약 조건을 만족시키면서 다양한 형태의 결함을 효과적으로 검출하였다.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.05c
• /
• pp.195-199
• /
• 2002

본 논문에서는 주어진 입력 이미지에서 얼굴 영역을 검출하기 위한 액티브 컨투어 모델(active contour models)을 제안한다. 제안한 모델은 스킨 칼라 모델(skin color model)에 의해 표현되는 사람 얼굴의 칼라 정보를 이용한다. 본 논문에서는 첨점(cusps), 모서리 (corners), 그리고 자동 위상 변화(automatic topological changes)를 고려한 레벨 셋 메소드(level set method)를 사용하여 액티브 컨투어를 진화시킨다. 실험 결과는 제안한 방법이 얼굴 영역 검출에 효과가 있음을 보여준다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2006.05a
• /
• pp.264-267
• /
• 2006

One of the serious problems that make the flaw identification in a multi-layered thick composite panel more difficult is the interferenceeffect of the upper layer. To take care of such a problem, here we propose an image enhancement approach that can get rid of such an interference effect to ultrasonic C-scan images by a normalization of the acquired signals by a reference signals, and demonstrate its performance in the experiments. Specifically, three specimens with artificial flaws are prepared and ultrasonic C-scan images are acquired experimentally to eliminate the undesired interference effect. Cleat successes are observed in the present study demonstrating the high potential of the proposed algorithm as a practical image enhancement tool in many practical situations.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.5
• /
• pp.2400-2405
• /
• 2013

We detect the obstacle for the UGV(unmanned ground vehicle) from the compound image which is generated by stereo vision sensor masking the depth image and color image. Stereo vision sensor can gathers the distance information by stereo camera. The obstacle information from the depth compound image can be send to mobile robot and the robot can localize the indoor area. And, we test the performance of the mobile robot in terms of distance between the obstacle and the robot's position and also test the color, depth and compound image respectively. Moreover, we test the performance in terms of number of frame per second which is processed by operating machine. From the result, compound image shows the improved performance in distance and number of frames.