• Journal of Welding and Joining
• /
• v.13 no.2
• /
• pp.42-52
• /
• 1995

In the evaluation of aging degradation on the structural materials based on the fracture mechanics, the detection and size prediction of defect are very important. Aiming at nondestructive detection and size prediction ol defect with high accuracy and resolution, therefore, an lnduced Current Focusing Potential Drop(ICFPD) technique has been developed. The principle of this technique is to induce a focusing current at an exploratory region by an induction wire flowing an alternating current(AC) that is a constant ampere and frequency. Defects are assessed with the potential drops that are measured the induced current on the surface of metallic material by the potential pick-up pins. In this study, the lCFPD technique was applied for evaluating the location and size of the surface crack and blind crack made in plate specimens, and also for detecting the defects existing in valve, a field component, that were developed by SCC etc. during the service. The results of this present study show that surface crack and blind crack are able to defect with potential drop. these cracks are distinguished with the distribution of potential drop, and the crack depths can be estimated with each normalized potential drop that are parameters estimating the depth of each type crack. In the field component, the defects estimated by experiment result correspond with those in the cutting face of the measuring point within a higher sensitivity.

• Journal of Biosystems Engineering
• /
• v.20 no.4
• /
• pp.368-375
• /
• 1995

A computer vision system was built to generate images of a stationary egg. This system includes a. CCD camera, a frame grabber, and an incandescent back lighting system An image processing algorithm was developed to accurately detect surface holes and surface cracks on eggs. With 20W of incandescent back light, the detection rate was shown to be the highest. The Sobel operator was found to be the best among various enhancing filters examined. The threshold value to distinguish between the crack and the translucent spots was found to be linear with the average gray level of a whole egg image. Those values of both gray level and area were used as criteria to detect holes in egg and those values of both area and roundness were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. On the average, it took 59.5 seconds to analyze an egg image and determine whether or not it was defected.

• Journal of Biosystems Engineering
• /
• v.39 no.3
• /
• pp.166-173
• /
• 2014

Purpose: In this study, we focused on the image processing method to determine the external quality of Fuji apples by identifying surface defects such as scabs and bruises. Method: A CCD camera was used to capture filter images with 24 different wavelengths ranging between 530 nm and 1050 nm. Image subtraction and division operations were performed to distinguish the defect area from the normal areas including calyx, stem, and glaring on the apple surface image. All threshold values of the image were examined to reveal the defect area of pretreated filter images. Results: The developed operation methods were [image (720 nm) - image (900 nm)]/image (700 nm) for bruise detection and [image (740 nm) - image (900 nm)]/image (590 nm) for scab detection, which revealed 81% and 90% recognition ratios, respectively. Conclusions: Our results showed several optimal wavelengths and image processing methods to detect Fuji apple surface defects such as bruises and scabs.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.577-586
• /
• 2022

Detection and classification of steel surface defects are critical for product quality control in the steel industry. However, due to its low accuracy and slow speed, the traditional approach cannot be effectively used in a production line. The current, widely used algorithm (based on deep learning) has an accuracy problem, and there are still rooms for development. This paper proposes a method of steel surface defect detection combining EfficientNetV2 for image classification and YOLOv5 as an object detector. Shorter training time and high accuracy are advantages of this model. Firstly, the image input into EfficientNetV2 model classifies defect classes and predicts probability of having defects. If the probability of having a defect is less than 0.25, the algorithm directly recognizes that the sample has no defects. Otherwise, the samples are further input into YOLOv5 to accomplish the defect detection process on the metal surface. Experiments show that proposed model has good performance on the NEU dataset with an accuracy of 98.3%. Simultaneously, the average training speed is shorter than other models.

• Steel and Composite Structures
• /
• v.50 no.1
• /
• pp.89-105
• /
• 2024

Multichannel analysis of surface waves (MASW) method has exhibited broad application prospects in the nondestructive detection of interfacial debonding in steel-concrete composite structures (SCCS). However, due to the structural diversity of SCCS and the high stealthiness of interfacial debonding defects, the feasibility of MASW method needs to be investigated in depth. In this study, synthetic parametric study on MASW nondestructive debonding detection for SCCSs is performed. The aim is to quantitatively analyze influential factors with respect to structural composition of SCCS and MASW measurement mode. First, stress wave composition and propagation process in SCCS are studied utilizing 2D numerical simulation. For structural composition in SCCS, the thickness variation of steel plate, concrete core, and debonding defects are discussed. To determine the most appropriate sensor arrangement for MASW measurement, the effects of spacing and number of observation points, along with distances between excitation points, nearest boundary, as well as the first observation point, are analyzed individually. The influence of signal type and frequency of transient excitation on dispersion figures from forwarding analysis is studied to determine the most suitable excitation signal. The findings from this study can provide important theoretical guidance for MASW-based interfacial debonding detection for SCCS. Furthermore, they can be instrumental in optimizing both the sensor layout design and signal choice for experimental validation.

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

• The Journal of the Korea Contents Association
• /
• v.11 no.3
• /
• pp.18-25
• /
• 2011

In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.3
• /
• pp.19-26
• /
• 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 Korean Society For Composite Materials Conference
• /
• 2002.05a
• /
• pp.35-38
• /
• 2002

Real defects in composite structures were detected by using laser measuring system. Four types of specimens, that is, a composite laminate, a honeycomb structure, a free-edge delamination and an adhesive joint, were used to study the applicability of ESPI and Shearography to composite structures. Thermal loading method, which can easily induce the surface deformation of specimen, was used to detect defects. Experimental results show that defects in composite structures can be easily detected by ESPI and Shearography. Moreover, it shows that ESPI and Shearography can be usefully applied to the detection of defects in various kinds of composite structures.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.18 no.6
• /
• pp.445-454
• /
• 1998

In order to establish a technical concept for the detection of defects in weldments in thin steel plate, an experimental and theoretical investigation was carried out for artificial defects in a steel plate having a thickness of 2.4mm by using the guided wave technique. In particular the goal was to find the most effective testing parameters paying attention to the relationship between the excitation frequency by a tone burst system and various incident angles. It was found that the test conditions that worked best was for a frequency of 840kHz and an incident angle of 30 or 85 degrees, most of the defects were detected with these conditions. Also, it was clear that a guided wave mode generated under an incident angle of 30 degrees was a symmetric mode, So, and that of 85 degrees corresponded to an antisymmetric mode, Ao. By using the two modes, most of all of the defects could be detected. Furthermore, it was shown that the antisymmetric mode was more sensitive to defects near the surface than the symmetric mode. Theoretical predictions confirmed this sensitivity improvement with Ao for surface defects because of wave structure variation and energy concentration near the surface.