• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.661-669
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• 2019

High frequency active sonar is appropriate for detecting small targets such as a diver in coast environment. In case of using high frequency active sonar in shallow coastal environment, a false alarm rate is high due to clutters caused by marine biological noise, ship noise, wake, etc. In this paper, we propose an algorithm for target detection which is robust against clutter in active sonar system in shallow coastal environment. The proposed algorithm increases the rate of reduction clutter using calculation of statistical characteristics of signal and a clustering method. The algorithm is evaluated and analysed with sea trial data, as a result, that shows the rate of reducing rate of clutter of 96 % and over.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.445-452
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• 2008

Preliminary results are reported on ship detection using coherence images computed from cross-correlating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, cross-correlation of multi-look images yields the different degrees of coherence between the images and water. In this paper, the polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV images, In the next step, we examine the technique when the dual-polarization data are split into two multi-look images, It was shown that the inter-look cross-correlation method could be applicable in the performance improvement of small ship detection and the land masking, It was also found that a simple combination of coherence images from each co-polarised (HH) inter-look and cross-polarised (HV) inter-look data can provide much higher target-detection possibilities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.113-117
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• 2014

This paper comparatively analyze to integration case to have a influence detection range estimation about radar cross section in radar system. This paper estimate detection range used to probability of detection in radar equation that used to swerling case 1 in case of radar cross section is small and used to swerling case 3 in case of radar cross section is large. Through simulation, coherent integration and non-coherent integration about swerling case difference were comparatively analyzed. In the result of comparative analysis, non-coherent integration case is outstanding detection range and we known that coherent integration don't suitable for detection range estimation.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.103-114
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• 2019

Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.1
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• pp.34-39
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• 2013

In the advanced research deducted infrared thermography (IRT) test using 4 inch pipe with artificial wall-thinning defect to measure on the wall-thinned nuclear pipe components. This study conducted for defect detection condition of nuclear small-bore pipe research using deducted condition in the advanced research. Defect process is processed by change for defect length, circumferential direction angle, wall-thinning depth. In the used equipment IR camera and two halogen lamps, whose full power capacitany is 1 kW, halogen lamps and Target pipe experiment performed to the distance of the changed 1 m, 1.5 m, 2 m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length. artificial defect of 4 inch pipe is high reliability in the 2 m, but small-bore pipe is in the 1.5 m from the defect clearly was detected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.175-181
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• 2014

In this paper, we propose a robust clutter pre-thresholding method using background adaptive Threshold Map for the clutter rejection in the complex coastal environment. The proposed algorithm is composed of the use of Threshold Map's and method of its calculation. Additionally we also suggest an automatic decision method of Thresold Map's update. Experimental results on some sets of real infrared image sequence show that the proposed method could remove clutters effectively without any loss of detection rate for the aim target and reduce processing time dramatically.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.27-33
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• 2020

Recently, visual object detection and tracking has become a vital role in many different applications. It spans various applications like robotics, video surveillance, and intelligent vehicle navigation. Especially, in current situation where the use of UAVs is expanding widely, detection and tracking to soot down illegal UAVs flying over the sky at airports, nuclear power plants and core facilities is becoming a very important task. The remarkable method in object tracking is correlation filter based tracker like KCF (Kernelized Correlation Filter). But it has problems related to target drift in tracking process for long-term tracking. To mitigate the target drift problem in video surveillance application, we propose a tracking method which uses KCF, adaptive thresholding and Kalman filter. In the experiment, the proposed method was verified by using monochrome video sequences which were obtained in the operational environment of UAV.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1629-1630
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• 2006

This paper describes a micro total analysis system ($\mu$ TAS) for detecting and digesting the target protein which includes a bead based temperature controllable microchip and computer based controllers for temperature and valve actuation. We firstly combined the temperature control function with a bead based microchip and realized the on-chip sequential reactions using two kinds of beads. The PEG-grafted bead, on which RNA aptamer was immobilized, was used for capturing and releasing the target protein. The target protein can be chosen by the type of RNA aptamer. In this paper, we used the RNA aptamer of HCV replicase. The trypsin coated bead was used for digesting the released protein prior to the matrix assisted laser desorption ionization time of flight mass spectrometer (MALDI TOF MS). Heat is applied for release of the captured protein binding on the bead, thermal denaturation and trypsin digestion. PDMS microchannel and PDMS micro pneumatic valves were also combined for the small volume liquid handling. The entire procedures for the detection and the digestion of the target protein were successfully carried out on a microchip without any other chemical treatment or off-chip handling using $20\;{\mu}l$ protein mixture within 20 min. We could acquire six matched peaks (7% sequence coverage) of HCV replicase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.