• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.347-350
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• 2003

In this paper, a new automated defects detection method for a TFT-LCD panel is presented. An input image is preprocessed to lessen small abnormal noises and non-uniformity of the image. The adaptive multi-thresholds are used to detect Muras, which are the major defects occurred on TFT-LCD panels. Those are determined adaptively depending on the brightness and the brightness distribution of a local block. For the synthetic images and real Mura images, the proposed algorithm can effectively detect Muras in a reasonable time.

• Journal of Urban Science
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• v.11 no.2
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• pp.1-8
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• 2022

This study proposed how to identify window defects by using photos uploaded by occupants during the pre-handover inspection of mulch-family housing. A total of 1168 door images were acquired to generate training data and validation data. Subsequently, through the proposed algorithms, every pixel in images labeled a door was binarized using the OTSU threshold, and then dark pixels were identified as defects. Experimental results demonstrated that our computer vision-based defects identification method detects the door with a recall of 57.9%, and door defects with 63.6%. Although it is still a challenge to automatically identify building defects because of the distortion and brightness of photos, this study has the potential to support better defects management. Ultimately, the improved pre-handover inspection may lead to increased customer satisfaction.

• 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.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.224-235
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• 2017

Purpose: Reliability is the most important factor to detect defects as wind turbines are deployed in large blades. The methods of detecting defects are various, such as non-destructive inspection and thermal imaging inspection. We propose the phased array ultrasonic testing method of non-destructive testing. Methods: We propose the active pressure mechanism for wind power blade. The phase array ultrasonic inspection method is used for fault detection inner blade surface. Controlled pressure of mechanism with respect to z-axis is important for guarantee the result of phase array ultrasonic inspection. The model based control and proposed mechanism are utilized for overall system stability and effectiveness of system. Result: The result of proposed pressure mechanism B is more stable than A. Convergence speed is also faster than A. Conclusion: We confirmed the performance of the proposed constant pressure mechanism through experiments. Non-destructive testing was applied to the specimen to confirm the reliability of detecting defects.

• Corrosion Science and Technology
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• v.17 no.6
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• pp.292-300
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• 2018

The modeling of 3D finite elements based on CAD data has been used to detect sites of corrosion defects in buried pipes. The results generated sophisticated profiles of electrolytic potential and vectors of current distributions on the earth surface. To identify the location of defects in buried pipes, the current distribution on the earth surface was projected to a plane of incidence that was identical to the pipe locations. The locations of minimum electrolytic potential value were found. The results show adequate match between the locations of real and expected defects based on modeling. In addition, the defect size can be calculated by integrating the current density curve. The results show that the defect sizes were $0.74m^2$ and $0.69m^2$, respectively. This technology may represent a breakthrough in the detection of indirect damage in various cases involving multiple defects in size and shape, complex/cross pipe systems, multiple anodes and stray current.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• Proceedings of the KWS Conference
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• 2005.06a
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• pp.194-196
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• 2005

Ultrasonic guided waves are gaining increasing attention for the inspection of platelike and rodlike structures. At the same time, inspection methods that do not require contact with the test piece are being developed for advanced applications. This paper capitalizes on recent advances in the areas of guided wave ultrasonics and noncontact ultrasonics to demonstrate a superior method for the nondestructive detection of thinning defects simulating hidden corrosion in thin aluminum plates. The proposed approach uses EMAT(electro-magnetic acoustic transducer) for the noncontact generation and detection of guided plate waves. Interesting features in the dispersive behavior of selected guided modes are used for the detection of plate thinning. It is shown that mode cutoff measurements provide a qualitative detection of thinning defects. Measurement of the mode group velocity can be also used to quantify of thinning depth.

• Journal of the Korean Society of Safety
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• v.11 no.4
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• pp.29-33
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• 1996

In this study, specimens were prepared which have some defects on the buttweld joint of mild steel. In order to detect the defects of specimens, the following tests were put to : AUT and RT. When the results of the three tests were compared, the conclusion could be brought to as follows. 1) AUT outstrips RT in the abillity to detect plane defects like slags or cracks, but RT excels AUT in the ability to detect spheroidal defects like blowholes. 2) RT detects neither taper cracks nor very closed cracks, whereas AUT detects both of them. 3) AUT can detect at once plane defects like cracks and spheroidal defects like blowholes.

• Journal of the Semiconductor & Display Technology
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• v.9 no.3
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• pp.59-63
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• 2010

This paper proposes computer visual inspection algorithms for various LCD defects which are found in a manufacturing process. Modular vision processing steps are required in order to detect different types of LCD defects. Those key modules include RGB filtering for pixel defects, gray-scale morphological processing and Hough transform for line defects, and adaptive threshold for spot defects. The proposed algorithms can give users detailed information on the type of defects in the LCD panel, the size of defect, and its location. The machine vision inspection system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.

• Journal of the Korean Society for Railway
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• v.18 no.5
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• pp.401-409
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• 2015

Defects generated in a railroad track that guides the railroad vehicle have the characteristic of growing fast; as such, the detection technology for railroad track defects is very important because defects can eventually cause mass disasters like derailments. In this study, a speed-up test facility was fabricated to investigate the feasibility of using magnetic flux leakage (MFL) technology for defect detection in a railroad track under speed-up condition; a test was conducted using a railroad track specimen with defects. For this purpose, an MFL sensor head dedicated to the configuration of the railroad was designed and test specimens with artificial defects on their surfaces were manufactured. Using the test facility, a speed-up test ranging from 4km/h to 12km/h was performed and defects including locations were successfully detected from MFL signals induced by defects with enhanced visibility by differentiating raw MFL signals. In the future, it should be possible to apply this system to a high-speed railroad inspection car by improving the lift-off stability that is necessary for speed-up of the developed MFL sensor system.