• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.177-184
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• 2012

An optical defect detection method using ESPI(electronic speckle pattern interferometry) is proposed. ESPI is widely used as a non-contact measurement system which show deformation and phase map in real time. ESPI can be divided as the in-plane, out-of-plane and shearography by operation principle and target object and also divided with bulk type and optic fiber type by the optic configurations. This paper is focused on optic fiber type out-of-plane ESPI, which has the following advantages: (1) low cost; (2) reduction of the unreliable factors generated by separated optic components; (3) simplification of the optic configuration; (4) great reduction of volume; (5) flexibility, to be easily designed into different structures to adapt to inaccessible environments such as pipeline cavity and so on.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.46-55
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• 2005

In this paper, we realize the panoramic infrared warning system to detect the small threaten object and propose the infrared image enhancement method to improve the warning ability of this system. This system composes of the sense head unit, the signal processing unit, and so on. In the proposed system, the sense head unit acquires the panoramic IR image with 360 degree field of view(FOV) by rotating the thermal sensor. The signal processing unit divides panoramic image into four sub-images with 90 degree FOV and computes the adaptive plateau value by using statistical characteristics of each subimage. Then the histogram equalization is performed for each subimage by using the adaptive plateau value. We realize the signal Processing unit by using the DSP and FPGA to perform the proposed method in real time. Experimental results show that the proposed method has better discrimination and lower false alarm rate than the conventional methods in this warning system.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.13-22
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• 2007

In this paper, we propose an audience counting method in an auditorium based on pattern comparison. The previous counting methods based on object detection can't exactly count the audience in real time because auditorium has coarse illumination condition and so many audiences. Therefore, in this paper, we count the audience in an auditorium with fixed seats by the method which the pattern from each reference seat is compared to the pattern from each input seat. Especially, to overcome limitations based on either illumination or noise, two pattern comparison methods are efficiently employed and combined. One is based on the amplitude projection, and the other is based on Walsh-Hadamard Kernel. Walsh-Hadamard Kernel has the characteristic which complements amplitude projection. Therefore, we ran achieve the accurate counting in the presence of coarse illumination and noise. The experimental results show that our method performs well on sequences of images acquired in an auditorium. We also verify a realistic possibility for other applications applying our method to the parking positioning system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1949-1955
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• 2021

With the recent COVID-19 situation, countries are seriously feeling the need for medical personnel and their technologies. PDepending on the aging society, the number of medical staff is actually decreasing, and in order to solve this problem, research is needed to replace the part that does not require high expertise among actual medical practices performed by doctors. This paper describes and proposes actual research methods related to unmanned medical systems that use various deep learning image processing-based technologies to check the recovery status applicable to rehabilitation areas where medical staff should face patients directly. The proposed method replaces passive calculations such as a protractor or a method of drawing a line in a photograph, which is the method used for actual motion comparison. Since it is performed in real time, it helps to diagnose quickly, and it is easy for medical staff to provide necessary information because data on the degree of match of motion performance can be checked.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.1
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• pp.51-61
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• 2023

Sound can travel a long distance in the ocean; hence, acoustic instruments have been widely used for ocean observations in various fields such as bathymetric survey, object detection, underwater communication, and current measurements. Herein we introduce a pressure-recording inverted echo sounder (PIES) which is one of the most powerful instruments, moored at seafloor for ocean observation in physical oceanography. The PIES can measure various kinds of oceanic phenomena (currents, mesoscale eddies, internal waves, and sea surface height variabilities) and support acoustic telemetry and pop-up data shuttle (PDS) system for remote data acquisition. In this paper, we review uses of PIES and describe present and prospective system of PIES including remote data acquisition toward (quasi) real-time data recovery.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Smart Media Journal
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• v.11 no.11
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• pp.25-31
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• 2022

Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.