Nondestructive testing is a method of inspecting particular target objects without destructing them in industrial sites. Infrared thermal imaging is one of the nondestructive testing techniques. Among them, lock-in infrared thermography technique is a technique to detect a defect by generating a temperature difference of an object using periodic heat waves. This paper deals with the development of lock-in infrared thermography technique by using numerical analysis model for SM45C metal specimens. As a result, the appropriate frequency was determined for defect detection in SM45C metal specimen by using the established thermal behavior mechanism by periodic heat wave.

양상추는 신선편이 채소 중 가장 중요한 원료 중 하나이다. 양상추는 수확 및 저장 중에 발생한 갈변이나 물러짐 등 표면 결함 발생하기도 하며 또한 수확 중 애벌레나 민달팽이 등 이물질이 포함하기도 한다. 세척공정을 통해 이러 한 애벌레나 이물질을 제거하지만, 세척과정 중에 완벽하게 제거되지 않은 결함들은 작업자가 육안으로 판별하여 일 일이 수작업으로 제거하고 있다. 이러한 육안 선별은 노동력이 많이 소요되고 작업능률이 낮아 자동 선별 요구도가 높다. 본 연구에서는 초분광 영상 기술을 이용하여 신선편이 양상추의 결함 검출 시스템을 개발하였다. 이 시스템은 가시광 및 근적외선 영역의 반사광 영상 측정부, 시료 이송부, 시료 반전부, 결제거부로 구성되어 신선편이 양상추의 앞면과 뒷면 모두의 결함을 동시에 검출한다. 이 시스템을 이용하여 애벌레 검출 알고리즘을 개발하였으며, 그 결과 신선편이 양상추의 이물질 결함이 가능하였다.

The purpose of this paper is to find an limitation to detect the defect of damaged asphalt pavement structures for infrared thermography. We use heat source of a natural light to detect the defect efficiently. The heat source was applied to the asphalt specimens. Four asphalt specimens were used: one was the asphalt containing depth of 1cm internal timber, two was the asphalt containing depth of 2cm internal void, Three was the asphalt containing depth of 3cm internal timber and four was not the asphalt containing internal timber. It was found that the depth of 3cm internal timber could be detected by this method. In addition, we used the image processing to make the damage zone displayed clear in the image obtained from the thermographic operation.

A image defect detecting vision system for the automatic optical inspection of wafer has been developed. For the successful detection of various kinds of defects, the performance of two threshold selection methods are compared and the improved Otsu method is adopted so that it can handle both unimodal and bimodal distributions of the histogram equally well. An automatic defect detection software for practical use was developed with the function of detection of ROI, fast thresholding and area segmentation. Finally each defect pattern in the wafer is classified and grouped into one of user-defined defect categories and more than 14 test wafer samples are tested for the evaluation of detection and classification accuracy in the inspection system.

딥러닝(Deep Learning) 기술은 이미지 데이터를 비롯하여 텍스트 데이터, 음성 데이터 등을 학습시켜 특성을 추출하고 인식하기 위한 여러 분야에 적용하고 연구되고 있다. 내부에 존재하는 블레이드는 본체와 분리가 불가능하고, 내부의 매우 불리한 환경속에서 검출이 이루어져야 한다. 기존의 영상 검출 방법은 상당한 시간이 요구되며, 기술자들의 개인적 능력과 경험에 의존하고 있다. 본 연구에서는 내부 블레이드의 표면 결함을 효율적으로 검출하고 자동화하기 위하여 Faster R-CNN 알고리즘을 학습시켜 검출 모델을 구축하였다.

The damage detection method of blade systems largely depends on the personal ability of an inspector using a camera. Thus, this paper proposes a deep learning-based detection method that can rapidly and reliably identify and evaluate the damages on the blades.

In this study, an experimental study of Impulse Thermography was carried out on a concrete specimen with in-placed artificial defects at different depths and dimensions. Then, all the data were processed by Pulse Phase Thermography technique by performing Fast Fourier Transformation. The results were compared with the absolute contrast method.

The Infrared-Thermography Technique is widely used for nondestructive evaluation (NDE) of structures due to its convenience. Particularly, various techniques have been tried to detect imperfections such as surface defects and welding defects of steel structures. This paper is a part of a study to detect defects in weld zone in steel structures using Infrared Thermography techniques. Finally, defects were detected by comparing infrared thermal image and surface temperature data of defected area with healthy area.

The purpose of this paper is to find an limitation to detect the defect of damaged asphalt pavement structures for improved infrared thermography. We use heat source of a natural light to detect the defect efficiently. The heat source was applied to the asphalt specimens containing maximum depth of 3cm internal timber. It was found that the depth of 3cm internal timber could be detected by this method. In addition, we used the improved image processing to make the damage zone displayed clear in the image obtained from the thermographic operation.

본 논문의 목적은 열화상 비파괴 검사기법을 적용시 손상된 콘크리트 구조물의 표면온도를 증폭시키기 위해 사용되는 외부 열원의 효율성을 알아보기 위함이다. 원적외선램프와 할로겐램프의 적용성과 효율성을 서로 비교하였다. 이를 위해 전술한 두 개의 열원을 콘크리트의 내부공극과 FRP쉬트의 비부착 결함 시험체에 적용하였다. 본 연구결과, 원적외선램프가 할로겐램프보다 더 효율적인 것으로 파악되었다. 또한, 손상영역을 효과적으로 검출하기위해서 가우스 필터와 프리윗 마스크 화상처리기법을 혼합한 알고리즘을 제안하였다.