• Imaging Science in Dentistry
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• v.49 no.4
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• pp.265-272
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• 2019

Purpose: To compare the diagnostic accuracy of cone-beam computed tomography (CBCT) with that of parallel(PPA) and oblique projected periapical(OPA) radiography for the detection of different types of peri-implant bone defects. Materials and Methods: Forty implants inserted into bovine rib blocks were used. Thirty had standardized bone defects(10 each of angular, fenestration, and dehiscence defects), and 10 were defect-free controls. CBCT, PPA, and OPA images of the samples were acquired. The images were evaluated twice by each of 2 blinded observers regarding the presence or absence and the type of the defects. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were determined for each radiographic technique. The 3 modalities were compared using the Fisher exact and chi-square tests, with P<0.05 considered as statistical significance. Results: High inter-examiner reliability was observed for the 3 techniques. Angular defects were detected with high sensitivity and specificity by all 3 modalities. CBCT and OPA showed similar AUC and sensitivity in the detection of fenestration defects. In the identification of dehiscence defects, CBCT showed the highest sensitivity, followed by OPA and PPA, respectively. CBCT and OPA had a significantly greater ability than PPA to detect fenestration and dehiscence defects(P<0.05). Conclusion: The application of OPA radiography in addition to routine PPA imaging as a radiographic follow-up method for dental implantation greatly enhances the visualization of fenestration and dehiscence defects. CBCT properly depicted all defect types studied, but it involves a relatively high dose of radiation and cost.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1519-1527
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• 2015

TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1073-1074
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• 2010

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.594-597
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• 2005

This paper shows that the defect detection in TFTarray panel can be improved by using newly developed software solution without adding additional hardware instruments. Some issues are reviewed in current TFT array test and new algorithm is explained for detecting more real defects without paying the penalty of reporting more false defects in TFT array test.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.193-199
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• 2015

Active thermography techniques have the capability of inspecting a broad range simultaneously. By evaluating the phase difference between the defected area and the healthy area, the technique indicates the qualitative location and size of the defect. Previously, the development of the defect detection method used a variety of materials and the test specimen was done. In this study, the proposed technique of lock-in is verified with artificial specimens that have different size and depth of subsurface defects. Finally, the defect detection capability was evaluated using comparisons of the phase image and the amplitude image according to the size and depth of defects.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.130-137
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• 2016

Non-destructive testing methods for composite materials (e.g., carbon fiber-reinforced and glass fiber-reinforced plastic) have been widely used to detect damage in the overall industry. This study detects defects using optical infrared thermography. The transient heat transport in a solid body is characterized by two dynamic quantities, namely, thermal diffusivity and thermal effusivity. The first quantity describes the speed with thermal energy diffuses through a material, whereas the second one represents a type of thermal inertia. The defect detection rate is increased by utilizing a lock-in method and performing a comparison of the defect detection rates. The comparison is conducted by dividing the irradiation method into reflection and transmission methods and the irradiation time into 50 mHz and 100 mHz. The experimental results show that detecting defects at 50 mHz is easy using the transmission method. This result implies that low-frequency thermal waves penetrate a material deeper than the high-frequency waves.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1445-1454
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• 1995

In this study, we have developed a prototype of the automatic defects detection system for capsule inspection using the computer vision techniques. The subjects for inspection are empty hard capsules of various sizes which are made of gelatine. To inspect both sides of a capsule, 2-stage recognition is performed. Features we have used are various lengths of a capsule, area, linearity, symmetricity, head curvature and so on. Decision making is performed based on average value which is computed from 20 good capsules in training and permission bounds in factories. Most of time-consuming process for feature extraction is computed by hardware to meet the inspection speed of more than 20 capsules/sec. The main logic for control and arithmetic computation is implemented using EPLD for the sake of easy change of design and reduction in time for developement. As a result of experiment, defects on size or contour of binary images are detected over 95%. Because of dead zone in imaging system, detection ratio of defects on surface, such as bad joint, chip, speck, etc, is lower than the former case. In this case, detection ratio is 50-85%. Defects such as collet pinch and mashed cap/body seldom appear in binary image, and detection ratio is very low. So we have to process the gray-level image directly in partial region.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.10
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• pp.1010-1019
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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 the 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.