• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.426-431
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material in real time. In this paper a realtime detection of the brazing defect of thin Inconel plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (23 kHz) ultrasonic transducer was used to infuse the welded Inconel plates with a short pulse of sound for 280 ms. The ultrasonic source has a maximum power of 2 kW. The surface temperature of the area under inspection is imaged by an infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the bound between the two faces of the Inconel plates near the defective brazing point and heated up highly, are observed. And the weak thermal signal is observed at the defect position of brazed plate also. Using the image processing technology such as background subtraction average and image enhancement using histogram equalization, the position of defective brazing regions in the thin Inconel plates can be located certainly.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.437-446
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• 2012

A face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into $5{\times}5$ blocks and depth values are averaged in each block. Then, $5{\times}5$ block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the $5{\times}5$ template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.445-451
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• 2021

In this study, we described the production of an antibody to living modified organisms (LMOs) containing the gene encoding for 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) from Agrobacterium tumefaciens strain CP4 EPSPS provides resistance to the herbicide glyphosate (N- (phosphonomethyl) glycine). These LMOs were approved and have recently been used in the feed, food production, and processing industries in South Korea. Highly efficient monoclonal antibody (mAb) production is crucial for developing assays that enable the proper detection and quantification of the CP4 EPSPS protein in LMOs. This study describes the purification and characterization of recombinant CP4 EPSPS protein in E. coli BL21 (DE3) based on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and matrixassisted laser desorption/ionization time-of-flight mass spectrometry. The production of mAbs was undertaken based on the standard operating procedure of Abclon, Inc.(South Korea), and the purity of the mAbs was assessed using SDS-PAGE. The following five mAb clones were produced: 2F2, 4B9, 6C11, 10A9, and 10G9. To verify the efficiency and specificity of the five developed mAbs, we performed Western blotting analysis using the LM (living modified) cotton crude extracts. All mAbs could detect the CP4 EPSPS protein in the LM cotton traits MON1445 and MON88913 with high specificity, but not in any other LM cottons or non-LM cottons. These data indicate that these five mAbs to CP4 EPSPS could be successfully used for the further development of antibody-based detection methods to target CP4 EPSPS protein in LMOs.

• Korean Journal of Optics and Photonics
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• v.34 no.4
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• pp.139-150
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• 2023

Despite various light detection and ranging (LiDAR) architectures, it is very difficult to achieve long-range detection and high resolution in both vertical and horizontal directions with a wide field of view (FOV). The scanning architecture is advantageous for high-performance LiDAR that can attain long-range detection and high resolution for vertical and horizontal directions. However, a large-area photodetector (PD), which is disadvantageous for detection speed, is essentially required to secure the wide FOV. Thus we propose a PD based on the static unitary detector (STUD) technique that can operate multiple small-area PDs as a single large-area PD at a high speed. The InP/InGaAs STUD PIN-PD proposed in this paper is fabricated in various types, ranging from 1,256 ㎛×949 ㎛ using 32 small-area PDs of 1,256 ㎛×19 ㎛. In addition, we measure and analyze the noise and signal characteristics of the LiDAR receiving board, as well as the performance and sensitivity of various types of STUD PDs. Finally, the LiDAR receiving board utilizing the STUD PD is applied to a 3D scanning LiDAR prototype that uses a 1.5-㎛ master oscillator power amplifier laser. This LiDAR precisely detects long-range objects over 50 m away, and acquires high-resolution 3D images of 320 pixels×240 pixels with a diagonal FOV of 32.6 degrees simultaneously.

• The Journal of Bigdata
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• v.7 no.2
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• pp.31-41
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• 2022

Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.345-354
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• 2005

Since motion estimation and motion compensation methods remove the redundant data to employ the temporal redundancy in images, it plays an important role in digital video compression. Because of its high computational complexity, however, it is difficult to apply to high-resolution applications in real time environments. If we have a priori knowledge about the motion of an image block before the motion estimation, the location of a better starting point for the search of an exact motion vector can be determined to expedite the searching process. In this paper presents the motion detection algorithm that can run robustly about recusive motion. The motion detection compares and analyzes two frames each other, motion of whether happened judge. Through experiments, we show significant improvements in the reduction of the computational time in terms of the number of search steps without much quality degradation in the predicted image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1987-1992
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• 2010

This paper deals with the method of using the three dimensional characteristic information to classify the front environment in travelling by using the images captured by a CCD camera equipped on a mobile robot. The images detected by the three dimensional characteristic information is divided into the part of obstacles, the part of corners, and th part of doorways in a corridor. In designing the travelling path of a mobile robot, these three situations are used as an important information in the obstacle avoidance and optimal path computing. So, this paper proposes the method of deciding the travelling direction of a mobile robot with using input images based upon the suggested algorithm by preprocessing, and verified the validity of the image information which are detected as obstacles by the analysis through neural network.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.55-62
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• 2011

In this paper, a temporal color and luminance variation suppression algorithm for a digital video sequence is proposed by considering time-varying light source. When a video sequence is sampled with the periodically emitting illuminant and with a short exposure time, the color rolling phenomenon occurs, where the color and the luminance of the image periodically change from field to field. In conventional signal processing techniques, the luminance variation remaining in the resultant video sequence degrades the constancy of the image sequence. In the proposed method, we obtain video sequences with constant luminance and color by compensating for the inter-field luminance variation. Based on a motion detection technique, the amount of the luminance variation for each channel is estimated on the background of the sequence without the effects of moving objects. The experimental results clearly show that our strategy efficiently estimated the illuminant change without being affected by moving objects, and the variations were efficiently reduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2541-2546
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• 2013

In this paper, we proposed on the azimuth correction method for a line-of-sight data-link tracking antenna system. Tracking antenna system is essential to maintain line-of-sight between moving object and data-link equipment. In order to calculate the azimuth and elevation between the moving object and antenna system, we used GPS data. also to match the each coordinate systems, we used geomagnetic sensor or beacon. However, the geomagnetic disturbance-prone terrain in places difficult to correct calibration. The first step, finds the location of the strongest RF signal, we should remember the difference between the reference point and the detected position of the antenna. The second step, we could communicate each other. And the azimuth angle is calculated by GPS values. Despite the geomagnetic interference, we can correct the azimuth angle quickly and easily.