• Journal of the Semiconductor & Display Technology
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• v.9 no.2
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• pp.79-85
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• 2010

In this paper, we propose a web inspection system (WIS) for real-time detection of paper defects which can cause critical fractures during papermaking process. Our system incorporates high speed line-scan camera, lighting system, and detection algorithm to provide robust and precise detection of paper defects in real-time. Since edge defects are very crucial to the paper fractures, our system focuses on the edge region of the paper instead of inspecting the whole paper area. In our algorithm, image projection and sub-pixel operation are utilized to detect the edge defects precisely and connected component labeling and shape analysis techniques are adopted to extract various kinds of the region defects. Experimental results revealed that our web inspection system is very efficient for detecting paper defects during papermaking processes.

• Proceedings of the Safety Management and Science Conference
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• 2004.05a
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• pp.283-289
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• 2004

The number of pixel defects including bright and black dots on a panel is one of the critical factors determining the quality of TFT-LCD. Since pixel defects on the TFT-LCD panels are sometimes unavoidable, manufacturers have to inspect the panels so that any panel with an unacceptable number of defects will not be delivered to the buyers. However, the buyers demand for the manufacturers to meet different pixel defects tolerances (acceptable number of pixel defects on a TFT-LCD panel) around central(tight) and peripheral(loose) inspection zones. The disagreement in quality standard among different buyers also cause confusions in screening non-confirmative products and unstable yield of production. Few research has focused on the effects of defect locations on a TFT-LCD panel on their detection probabilities and the rational division of defect inspection zones. In this research, experiments were conducted to find the detection probabilities of black dot defects with respect to their varying locations on a TFT-LCD. It is proposed a rational division of inspection zone on a TFT-LCD panel on the basis of detection probabilities of the defects. With these division of inspection zones and the mean defect detection probability within each zone, it is expected to establish a more reasonable pixel defects tolerances.

• Journal of the Korean Society of Safety
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• v.10 no.4
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• pp.9-12
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• 1995

Detection of small defects by medical ultrasonic testing when the thermal sprayed coating by $MoSi_2$ on the metal is done. The defects may occur at the bonded surface. So, the detecting method of the defects by non-destructive in spection is desired. Here, in order to examine the possibility of the detection of the small defects by the ultrasonic. The electronic scanning ultrasonic equipment using an array probe developed as the medical ultrasonic diagnostic equipment is applied for the detection of the metal material defects. It's validity is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.3-35
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• 2001

Developed is an optical auto-inspection system to detect some microscopic defects on the Inside surface of the hydraulic automobile brakes at the production line. A small cylindrical detection module with a solid laser source at its center has two rings of optical fibers to separately collect light reflected and scattered from the defects on the surface. The cylindrical brake part rotates with respect to the detection module that will move parallel to the rotational axis of the cylinder. Thus, the optical module can scan the whole inside surface area. The automatic detection of the defects is to compare the intensity distributions ...

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

• Journal of IKEEE
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• v.18 no.2
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1100-1104
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• 2017

A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1175-1182
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• 2013

The lock-in mode infrared thermography (IRT) technique has been developed to improve the detection capability of defects in materials with high thermal conductivity, and it has been shown to provide better detection capability than conventional active IRT. Therefore, to investigate the application of this technique to nuclear piping components, lock-in mode IRT tests were conducted on pipe specimens containing simulated wall-thinning defects. Phase images of the wall-thinning defects were obtained from the tests, and they were compared with thermal images obtained from conventional active IRT tests. It showed that the ability to size the detected wall-thinning defects in piping components was improved by using lock-in mode IRT. The improvement was especially apparent when detecting short and narrow defects and defects with slanted edges. However, the detection capability for shallow wall-thinning defects did not improve much when using lock-in mode IRT.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.140-145
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• 2010

This study presents a detection method for mechanically fabricated defects on underwater steel pipes, using ultrasonic guided waves. Three different diameters (60, 90, and 114 mm) of 1000-mm long steel pipes were considered, along with several experimental design factors such as incident angles, incident distances, and the degrees of defects, to investigate how these factors affected the experimental results - the detectability of the mechanical defects. From the experimental results, we determined that the amplitude and arrival time of the first received wave signals gave a promising clue for distinguishing the existence of the defects and their severities. Between the amplitude and arrival time, the arrival time gave a more promising indication since it was affected by the experimental factors in a constant manner. Therefore, it was shown that the use of ultrasonic guided waves for underwater pipe inspection is feasible.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.617-625
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• 2012

Non-contact detection of internal defects in a rail using ultrasonic waves is discussed in this paper. Cracks in a rail may be a cause of a serious railway accident such as derailment if left undetected. Concurrent rail inspection method based on ultrasonic testing using piezoelectric transducers has several limitations as it should keep physical contact with the rail. This work suggests a non-contact detection of internal defects in a rail using ElectroMagnetic Acoustic Transducers (EMAT) which can produce and measure ultrasonic waves in a rail without any couplant. The EMATs for rail inspection are designed and fabricated and their working performance is verified through a series of experiments on various types of internal defects in test rails. The effect of lift-off between the transducers and the rail on the generated signals is also discussed.