• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.5
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• pp.508-515
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• 2011

Bovine incisor was investigated using active infrared thermography(IRT) to visualize crack on bovine teeth. An artificial crack was carefully created in bovine incisor sample by compression load of universal tensile machine. While applying a sinusoidal heat wave to the cracked bovine incisor through halogen lamp, consecutive digital infrared images was captured from the sample surface at a frequency synchronized with heat excitation. Phase information of thermal image was calculated by four-point correlation method and processed to produce the phase image of bovine incisor. This phase image showed clearly the crack on the incisor, which was hardly detected in traditional passive thermography.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1310-1310
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• 2015

대단위 태양광 발전소 또는 고층건물에 설치된 태양광 모듈의 결함을 분석하는데 있어 열화상 카메라를 통한 온도로써 태양광 모듈의 결함을 검출하는 방식이 대두되고 있다. 본 논문에서는 열화상 카메라로 얻은 영상을 온도로 표현하는데 필요한 영상처리를 각각의 태양광 모듈들을 셀 단위로 분류하고 해당 셀을 기준으로 행 이미지를 ROI로 잡은 후 이미지 저장을 하는 방법을 제안한다.

• Journal of IKEEE
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• v.22 no.3
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• pp.578-584
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• 2018

Image segmentation has been presented in the various method in image interpretation and recognition, and the image is using separate the characteristics of the specific purpose. In this paper, we proposed an algorithm that separate image for feature points detected to high temperature in a Thermal infrared image. In order to improve the processing time, the proposed algorithm is implemented to FPGA Hardware Block using the Zynq-7000 Evaluation Board environment. The proposed High-Temperature Detection Algorithm and total FPGA blocks show a decrease of a processing time result from 16ms to 0.001ms, and from 50ms to 0.322ms respectively. It is also verified similar results of the PSNR to comparing software thermal testbench and hardware ones.

• 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 Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.11-18
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• 2022

The use of multi-spectral cameras is essential for day and night pedestrian detection. In this paper, a color camera and a thermal imaging infrared camera were used to detect pedestrians near a crosswalk for 24 hours at an intersection with a high risk of traffic accidents. For pedestrian detection, the YOLOv5 object detector was used, and the detection performance was improved by using color images and thermal images at the same time. The proposed system showed a high performance of 0.940 mAP in the day/night multi-spectral (color and thermal image) pedestrian dataset obtained from the actual crosswalk site.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.162-169
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• 2012

This study detected SCC defects of dissimilar metal welded(STS304 and SA106 Gr. b) pipes using the ultrasonic infrared thermography method and the lock-in image treatment method among infrared thermography method. The infrared excitement equipment has 250 Watt of output and 20 kHz of frequency. By using the ultrasound infrared thermography method, the internal defects of dissimilar metal weld joints of pipes used at nuclear power plants could get detected. By an actual PT test, it was observed that the cracks inside the pipe existed not as a single crack but rather as a multiple cracks within a certain area and generated a hot spot image of a broad area on the thermography image. In addition, UT technology could not easily defects detected by the width of $10\;{\mu}m$ fine hair cracks. but, ultrasound infrared thermography technique was defect detected.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.1
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• pp.12-19
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• 2012

This study conducted infrared (IR) thermography tests using pipe and plate specimens with artificial wall-thinning defects to find an optimal condition for IR thermography test on the wall-thinned nuclear piping components. In the experiment halogen lamp was used to heat the specimens. The distance between the specimen and the lamp and the intensity of halogen lamp were regarded as experimental parameter. When the distance was set to 1~2 m and the lamp intensity was above 60 % of full power, a single scanning of IR thermography detected all artificial wall-thinning defects, whose minimum dimension was $2{\Theta}=90^{\circ}$, d/t=0.5, and $L/D_o=0.25$, within the pipe of 500 mm in length. Regardless of the distance between the specimen and the lamp, the image of wall-thinning defect in IR thermography became distinctive as the intensity of halogen lamp increased. The detectability of IR thermography was similar for both plate and pipe specimens, but the optimal test condition for IR thermography depended on the type of specimen.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.41-49
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• 2022

Recently interests on the application of thermal cameras have increased with the advance of image analysis technology. Aside from a simple image acquisition, applications such as digital twin and thermal image management systems have gained popularity. To this end, we studied the effect of emissivity on the DN (Digital Number) value in the process of derivation of a relational expression for converting DN to an actual surface temperature. The DN value is a number representing the spectral band value of the thermal image, and is an important element constituting the thermal image data. However, the DN value is not a temperature value indicating the actual surface temperature, but a brightness value indicating high and low heat as brightness, and has a non-linear relationship with the actual surface temperature. The reliable relationship between DN and the actual surface temperature is critical for a thermal image processing. We tested the relationship between the actual surface temperature and the DN value of the thermal image, and then the radiation adjustment was performed to better estimate actual surface temperatures. As a result, the relation graph between the actual surface temperature and the DN value similarly show linear pattern with the relation graph between the radiation-controlled non-contact thermometer and the DN value. And the non-contact temperature after adjusting the emissivity was closer to the actual surface temperature than before adjusting the emissivity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.96-106
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• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.