• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.4
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• pp.299-304
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• 2014

In this paper, a method is tested to measure temperatures in high-temperature welds. Protective glass was installed between an infrared thermal imaging camera and a heat source, and temperature compensation was applied to the measuring instruments. When the temperature of halogen lamps was taken in real-time and measured by the thermal camera, the temperature was found to be almost invariant with the distance between the camera and heat source. The temperature range could be predicted, through correlations with the thickness of the protective glass and the measured distance. This study suggests that the temperature measurement of welds obtained by using an infrared thermal imaging camera is valid, through experimental testing of heat sources.

• 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.30 no.2
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• pp.98-103
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• 2010

In this paper, imaging processing study obtained from CCD image and thermography image was performed in order to treat easily thermographic data without any risks of personnels who conduct the condition monitoring for the abnormal or failure status occurrable in industrial power plants. This imaging processing is also applicable to the predictive maintenance. For confirming the broad monitoring, a methodology producting single image from the panorama technique was developed no matter how many cameras are employed, including fusion method for discrete configuration for the target. As results, image fusion from quick realtime processing was obtained and it was possible to save time to track the location monitoring in matching the images between CCTV and thermography.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.5-7
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• 2019

열화상 시스템은 물체로부터 발산되는 적외선을 영상화하여 물체를 탐지하는 장점으로 인해 군사 분야는 물론 현재 민수 분야(자동차, Security 시스템)에 활용분야가 넓어지고 있다. 기존에는 대부분 FPGA 기반으로 열화상 열상 모듈을 개발하였지만 민수 분야에 다양한 요구사항 및 범용성에 유연한 대처가 힘든 실정이다. 따라서 다양한 요구사항과 범용성을 만족하기 위한 시스템의 필요성이 대두되었다. 본 논문에서는 상용 이미지 프로세서를 이용한 열화상 영상 처리 시스템을 제안한다. 제안된 시스템은 기존 FPGA 기반 시스템이 아닌 상용 이미지 프로세서를 사용함으로써 범용 영상 입·출력 인터페이스 및 각종 디바이스를 지원함에 따라 다양한 요구사항과 범용성을 만족한다. 따라서 시스템이 구축이 되면 뛰어난 접근성으로 인하여 시스템 추가/변경 시 기존의 시스템에 비해 개발 비용 및 기간을 단축할 수 있으며 그로 인하여 다양한 고객 요구사항 만족, 개발 비용 및 시간 단축, 제품 출시일 등 다양한 이점을 얻을 것으로 예상한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1315-1316
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• 2015

본 논문에서는 원자력발전소 중대 사고를 모사한 비가시 환경에서 적외선 열화상카메라의 관측성능 (가시도)을 정량적으로 평가하고자 하였다. 이를 위해 세계기상기구의 가시도 정의를 이용하여 가시도를 측정하였으며, 측정된 가시도 조건에서 적외선 열화상카메라의 관측성능을 평가하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.157-164
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• 2011

This work is devoted to the technique application of lock-in infrared Thermography in the shipbuilding and ocean engineering industry. For this purpose, an exploratory study to find the optimized test conditions is carried out by the design of experiments. It has been confirmed to be useful method that the phase contrast images were quantified by a reference image and weighted by defect hole size. Illuminated optical intensity of lower or medium strength give a good result for getting a phase contrast image. In order to get a good phase contrast image, lock-in frequency factors should be high in proportion to the illuminated optical intensity. The integration time of infrared camera should have been inversely proportional to the optical intensity. The other hand, the difference of specimen materials gave a slightly biased results not being discriminative reasoning.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.6
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• pp.466-473
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• 2016

In a shipbuilding process, thermal damage to the ship structure at the rear end results from an excessive heat input and conduction during welding process. To prevent such damage, appropriate control of the heat input, based on welding temperature measurement, is required. For temperature measurement, contact and non-contact methods are available; the thermography system is a popular non-contact temperature measurement. When the intensity of radiation from a high-temperature object is excessive, however, detecting the sensors of ordinary thermography systems leads to an inability in measuring the temperature due to saturation. Hence, this study suggests use of a neutral density filter that prevents an excessive amount of radiation from being accumulated in a thermography system, and thus makes it possible to quantitatively measure an object's temperature as high as $3000^{\circ}C$.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.1-10
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• 2021

The ability to recognize human emotions by computer vision is a very important task, with many potential applications. Therefore the demand for emotion recognition using not only RGB images but also thermal images is increasing. Compared to RGB images, thermal images has the advantage of being less affected by lighting conditions but require a more sophisticated recognition method with low-resolution sources. In this paper, we propose a Divide and Conquer-based CNN training strategy to improve the performance of facial thermal image-based emotion recognition. The proposed method first trains to classify difficult-to-classify similar emotion classes into the same class group by confusion matrix analysis and then divides and solves the problem so that the emotion group classified into the same class group is recognized again as actual emotions. In experiments, the proposed method has improved accuracy in all the tests than when recognizing all the presented emotions with a single CNN model.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.