• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.543-544
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• 2021

본 논문에서는 스마트 팩토리의 자동화 공정을 위하여 제품 자동 판별과 불량 시 작업자에게 자동으로 통보해주는 시스템을 설계한다. 생산라인의 효율을 극대화하기 위해서는 작업자의 개입이 적은 상태로 시스템에 의해서 자동으로 공정이 이루어져야 한다. 따라서 본 시스템을 적용해 작업자는 자동으로 돌아가는 라인에 크게 개입하지 않고 문제가 발생했을 때만 투입되어 조치할 수 있게 된다. 따라서 생산과 효율을 크게 증가시키면서 작업자의 실수를 미연에 방지하고 제품의 신뢰성을 향상시킬 수 있다.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.377-382
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• 2006

In this paper, two methods based on the use of morphology and pattern matching prior to detect classified defects automatically on the back light unit which is a part of display equipments are proposed. One is the template check method which detects small size defects by using closing and opening method, and the other is the block matching method which detects big size defects by comparing with four regions of uniform blocks. The TC algorithm also can detect defects on the non-uniform pattern of BLU by using revised Otsu method. The proposed method has been implemented on the automatic defect detection system we developed and has been tested image data of BLU captured by the system.

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.179-184
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• 2024

In the car industry, welding is a fundamental linking technique used for joining components, such as steel, molds, and automobile parts. However, accurate inspection is required to test the reliability of the welding components. In this study, we investigate the detection of weld beads using 2D image processing in an automatic recognition system. The sample image is obtained using a 2D vision camera embedded in a lighting system, from where a portion of the bead is successfully extracted after image processing. In this process, the soot removal algorithm plays an important role in accurate weld bead detection, and adopts adaptive local gamma correction and gray color coordinates. Using this automatic recognition system, geometric parameters of the weld bead, such as its length, width, angle, and defect size can also be defined. Finally, on comparing the obtained data with the industrial standards, we can determine whether the weld bead is at an acceptable level or not.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1030-1035
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• 2014

Machine vision inspection systems have replaced human inspectors in defect inspection fields for several decades. However, the inspection results of machine vision are often affected by small changes of illumination. When small changes of illumination appear in image histograms, the influence of illumination can be decreased by transformation of the histogram. In this paper, we propose an enhanced histogram matching algorithm which corrects distorted histograms by variations of illumination. We use the resolution resizing method for an optimal matching of input and reference histograms and reduction of quantization errors from the digitizing process. The proposed algorithm aims not only for improvement of the accuracy of defect detection, but also robustness against variations of illumination in machine vision inspection. The experimental results show that the proposed method maintains uniform inspection error rates under dramatic illumination changes whereas the conventional inspection method reveals inconsistent inspection results in the same illumination conditions.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.9
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• pp.92-99
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• 2003

Quality inspection of textile products is an important problem for fabric manufacturers. This paper presents an automatic vision-based system for quality control of web textile fabrics. Typical web material is 1-3m wide and is driven with speeds ranging from 20m/min to 200m/min. At the present, the quality assessment procedures are performed manually by expert. But worker can not detect more than 60％ of the present defect and inspect the fabric if moving faster than 30m/min. To increase the overall quality and homogeneity of textile, an automated visual inspection system is needed fur the productivity. However, the existing inspection system are too expensive to purchase for small companies. In this paper, the proposed PC based real-time inspection algorithm gives low cost textile inspection system, high detection rate with good accuracy and low rate of false alarms. The method shows good results in the detection of several types of fabric defects.

• Journal of Korea Multimedia Society
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• v.24 no.8
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.3
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• pp.92-99
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• 1999

The inspection unit which is developed and used in this study, is processed the shape data from the CCD camera to seek welding bite section shape, and then calculated as a real dimension from measuring the value of each inspection item. The reason of measuring with the real in this study is came out from the image method which used for a long time, which is extricated the characteristic as the dimension of pixel by recognize pixel. The measurement method of the section shape is that we decide the thresholding value after we drew the histogram to binarizate the object. After that, we make flat the object to get rid of the noise and measure the shape of welded part through the boundarization of the object. The shape measurement is that measure the value of the welding part to adapt the actual operation program from using the ratio between the actual dimension of the standard specimen and the dimension of image, to measure the ratio between the actual product and the camera image. The inspection algorithm which estimates the quality of welded product is developed and also, the software GUI(Graphic User Interface) which processes the automatic test function of the inspection system is developed. We make the foundation of the inspection automatic system and we will help to apply other welding machine.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.6 no.1
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• pp.29-36
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• 2010

Ultrasonic inspection of heavy walled cast austenitic stainless steel(CASS)welds is very difficult due to complex and coarse grained structure of CASS material. The large size of anisotropic grain strongly affects the propagation of ultrasound by severe attenuation, change in velocity, and scattering of ultrasonic energy. therefore, the signal patterns originated from flaws can be difficult to distinguish from scattered signals. To improve detection and sizing capability of ID connected defect for heavy walled CASS piping welds, the low frequency segmented TRL Pulse Echo and Phased Array probe has been developed. The experimental studies have been performed using CASS pipe mock-up block containing artificial reflectors(ID connected EDM notch). The automatic pulse echo and phase array technique is applied the detection and the length sizing of the ID connected artificial reflectors and the results for detection and sizing has been compared respectively. The goal of this study is to assess a newly developed ultrasonic probe to improve the detection ability and the sizing of the crack in coarse-grained CASS components.