• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.627-632
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• 2016

In this paper, we propose a new vision-based defect inspection system for the surface of hot slabs. To minimize the influence of self-emission from slab surfaces with high temperature, an optic method based on blue LED light and a blue pass filter is proposed. Because the slab surface is partially covered with scales, which are unavoidable oxidized substances caused during manufacturing, it is difficult to distinguish between vertical cracks and scale. In order to resolve this problem and to improve the detection performance, the use of a Gabor filter and dynamic programming are proposed. Finally, the effectiveness of the proposed method is shown by means of experiments conducted on images of hot slabs that were obtained from an actual slab production line.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.68-73
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• 2021

In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.18-23
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• 2006

The formation degree of sustain (ITO pattern) determines the quality of a PDP (Plasma Display Panel). Thus, in the present study, we attempt to detect 100% of the defects that are larger than $30{\mu}m$. Currently, the inspection method in the PDP manufacturing process is dependent upon the naked eye or a microscope in off-line mode. In this study, a prototype inspection system for PDP ITO patterned glass is developed. The developed system, which is based on a line-scan mechanism, obtains information on the defects and sorts the defects by type automatically. The developed inspection system adopts a multi-vision method using slit-beam formation for minimum inspection time and the detection algorithm is embodied in the detection ability. Characteristic defects such as pin holes, substances, and protrusions are extracted using the blob analysis method. Defects such as open, short, spots and others are distinguished by the line type inspection algorithm. It was experimentally verified that the developed inspection system can detect defects with reliability of up to 95% in about 60 seconds for the 42-inch PDP panel.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.55-62
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• 2013

Automatic inspection system is required for increment of copper plate production and demand expansion. Thus 3D surface form and defect detection of copper plate calls for 3D image and GUI analysis. Limitation of 2D analysis, such as error occurrence and decision difficulty makes eye inspection automatic. Automatic inspection is able to raise accurate inspection rate and productivity efficiency elevation. In this paper defect classification is defined and inspection system is implemented. Defect analysis algorithms and GUI for 3D image analysis is developed and tested.

• Proceedings of the KWS Conference
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• 2006.05a
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• pp.148-150
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• 2006

In this paper, the applicability of an UET(ultrasound excited thermography) for a defect detection of the welded receptacle is described. An UET(ultrasound excited thermography) is a defect-selective and fast imaging tool for damage detection. A high power ultrasound-excited vibration energy with pulse durations of 280ms is injected into the outer surface of the welded receptacle made of Al material. An ultrasound vibration energy sent into the welded receptacle propagate inside the sample until they are converted into the heat in the vicinity of the defect. The injection of the ultrasound excited vibration energy results in heat generation so that the defect is turned into a local thermal wave transmitter. Its local heat emission is monitored by the thermal infrared camera. And they are processed by the image recording system. Measurement was performed on aluminum receptacle welded by using Nd:YAG laser. The observed thermal image revealed two area of defects along the welded seam.

• Journal of the Korean Ceramic Society
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• v.43 no.4 s.287
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• pp.259-263
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• 2006

The applicability of UPT (Ultrasound Pulse Thermography) for real-time defect detection of the ceramic heating plate is described. The ceramic heating plate with superior insulation and high radiation is used to control the water temperature in underwater environment. The underwater temperature control system can be damaged owing to the short circuit, which resulted from the defect of the ceramic heating plate. A high power ultrasonic energy with pulse duration of 280 ms was injected into the ceramic heating plate in the vertical direction. The ultrasound excited vibration energy sent into the component propagate inside the sample until they were converted to the heat in the vicinity of the defect. Therefore, an injection of the ultrasound pulse wave which results in heat generation, turns the defect into a local thermal wave transmitter. Its local emission is monitored and recorded via the thermal infrared camera at the surface which is processed by image recording system. Measurements were Performed on 4 kinds of samples, composed of 3 intact plates and the defect plate. The observed thermal image revealed two area of crack in the defective ceramic heating plate.

• International Journal of Railway
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• v.1 no.1
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• pp.1-5
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• 2008

The railway system requires safety and reliability of service of all railway vehicles. Suitable technical systems and working methods adapted to it, which meet the requirements on safety and good order of traffic, should be maintained. For detection of defects, non-destructive testing methods-which should be quick, reliable and cost-effective - are most often used. Since failure in railway wheelset can cause a disaster, regular inspection of defects in wheels and axles are mandatory. Ultrasonic testing, acoustic emission and eddy current testing method and so on regularly check railway wheelset in service. However, it is difficult to detect a crack initiation clearly with ultrasonic testing due to noise echoes. It is necessary to develop a non-destructive technique that is superior to conventional NDT techniques in order to ensure the safety of railway wheelset. In the present paper, the new NDT technique is applied to the detection of surface defects for railway wheelset. To detect the defects for railway wheelset, the sensor for defect detection is optimized and the tests are carried out with respect to surface and internal defects each other. The results show that the surface crack depth of 1.5 mm in press fitted axle and internal crack in wheel could be detected by using the new method. The ICFPD method is useful to detect the defect that initiated in railway wheelset.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.513-520
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• 2020

Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.79-84
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• 2023

Image generation models have been applied in various fields to overcome data sparsity, time and cost issues. However, it has limitations in generating images from regular pattern images and detecting defects in such data. In this paper, we verified the feasibility of the image generation model to generate pattern images and applied it to data augmentation for defect detection of OLED panels. The data required to train an OLED defect detection model is difficult to obtain due to the high cost of OLED panels. Therefore, even if the data set is obtained, it is necessary to define and classify various defect types. This paper introduces an OLED panel defect data acquisition system that acquires a hypothetical data set and augments the data with an image generation model. In addition, the difficulty of generating pattern images in the diffusion model is identified and a possibility is proposed, and the limitations of data augmentation and defect detection data augmentation using the image generation model are improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.687-701
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• 2020

In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.