• Title/Summary/Keyword: Detection of spots and cracks

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Development of System Configuration and Diagnostic Methods for Tongue Diagnosis Instrument (설진 기기의 시스템 구성 및 진단 방법 개발)

  • Kim, Keun-Ho;Do, Jun-Hyeong;Ryu, Hyun-Hee;Kim, Jong-Yeol
    • Korean Journal of Oriental Medicine
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    • v.14 no.3
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    • pp.89-95
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    • 2008
  • A tongue shows physiological and clinicopathological changes of inner organs. Visual inspection of a tongue is not only convenient but also non-invasive. To develop an automat ic tongue diagnosis system for an objective and standardized diagnosis, the separation of the tongue are a from a facial image and the detection of coatings, spots and cracks are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth as well as those of tongue furs and body are similar. The propose d method includes preprocessing with down-sampling and edge enhancement, over-segmentation, detecting positions with a local minimum over shading from the structure of a tongue, and correcting local minima or detecting edge with color difference. The proposed method produces the region of a segmented tongue, and then decomposes the color components of the region into hue, saturation and brightness, resulting in classifying the regions of tongue furs(coatings) into kinds of coatings and substance and segmenting them. Spots are detected by using local maxima and the variation of saturation, and cracks are searched by using local minima and the directivity of dark areas in brightness. The results illustrate the segmented region with effective information, excluding a non-tongue region and also give us accurate discrimination of coatings and the precise detection of spots and cracks. It can be used to make an objective and standardized diagnosis for an u-Healthcare system as well as a home care system.

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Automatic crack detection of dam concrete structures based on deep learning

  • Zongjie Lv;Jinzhang Tian;Yantao Zhu;Yangtao Li
    • Computers and Concrete
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    • v.32 no.6
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    • pp.615-623
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    • 2023
  • Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

Detection of Surface Defects in Eggs Using Computer Vision (컴퓨터 시각을 이용한 계란 표면의 결함 검출)

  • Cho, H.K.;Kwon, Y.
    • Journal of Biosystems Engineering
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    • v.20 no.4
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    • pp.368-375
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    • 1995
  • A computer vision system was built to generate images of a stationary egg. This system includes a. CCD camera, a frame grabber, and an incandescent back lighting system An image processing algorithm was developed to accurately detect surface holes and surface cracks on eggs. With 20W of incandescent back light, the detection rate was shown to be the highest. The Sobel operator was found to be the best among various enhancing filters examined. The threshold value to distinguish between the crack and the translucent spots was found to be linear with the average gray level of a whole egg image. Those values of both gray level and area were used as criteria to detect holes in egg and those values of both area and roundness were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. On the average, it took 59.5 seconds to analyze an egg image and determine whether or not it was defected.

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Quality Inspection and Sorting in Eggs by Machine Vision

  • Cho, Han-Keun;Yang Kwon
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1996.06c
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    • pp.834-841
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    • 1996
  • Egg production in Korea is becoming automated with a large scale farm. Although many operations in egg production have been and cracks are regraded as a critical problem. A computer vision system was built to generate images of a single , stationary egg. This system includes a CCD camera, a frame grabber board, a personal computer (IBM PC AT 486) and an incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. Fro a sample of 300 eggs. this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs v ewed from above. Those two values were used as criteria to sort eggs. Accuracy in grading was found to be 96.7% as compared with results from weight by electronic scale.

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Detection Algorithm for Cracks on the Surface of Tomatoes using Multispectral Vis/NIR Reflectance Imagery

  • Jeong, Danhee;Kim, Moon S.;Lee, Hoonsoo;Lee, Hoyoung;Cho, Byoung-Kwan
    • Journal of Biosystems Engineering
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    • v.38 no.3
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    • pp.199-207
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    • 2013
  • Purpose: Tomatoes, an important agricultural product in fresh-cut markets, are sometimes a source of foodborne illness, mainly Salmonella spp. Growth cracks on tomatoes can be a pathway for bacteria, so its detection prior to consumption is important for public health. In this study, multispectral Visible/Near-Infrared (NIR) reflectance imaging techniques were used to determine optimal wavebands for the classification of defect tomatoes. Methods: Hyperspectral reflectance images were collected from samples of naturally cracked tomatoes. To classify the resulting images, the selected wavelength bands were subjected to two-band permutations, and a supervised classification method was used. Results: The results showed that two optimal wavelengths, 713.8 nm and 718.6 nm, could be used to identify cracked spots on tomato surfaces with a correct classification rate of 91.1%. The result indicates that multispectral reflectance imaging with optimized wavebands from hyperspectral images is an effective technique for the classification of defective tomatoes. Conclusions: Although it can be susceptible to specular interference, the multispectral reflectance imaging is an appropriate method for commercial applications because it is faster and much less expensive than Near-Infrared or fluorescence imaging techniques.

Crack Detection and Sorting of Eggs by Image Processing (영상처리에 의한 계란의 파란 검출 및 선별)

  • Cho, H.K.;Kwon, Y.;Cho, S.K.
    • Korean Journal of Poultry Science
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    • v.22 no.4
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    • pp.233-238
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    • 1995
  • A computer vision system was built to generate images of a single, stationary egg. This system includes a CGD camera, a frame grabber, and incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs viewed from above. Those two values were used as criteria to sort eggs. The coefficients of determination(r$^2$) for the regression equations between weights and those two values were 0.967 and 0.972 in the two sets of experiment. Accuracies in grading were found to be 95.6% and 96.7% as compared with results from sizing by electronic weight scale.

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Fatigue Crack Detection Test of Weldments Using Piezoceramic Transducers

  • KIM MYUNG HYUN;KANG SUNG WON;KEUM CHUNG-YON
    • Journal of Ocean Engineering and Technology
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    • v.19 no.4 s.65
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    • pp.21-27
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    • 2005
  • Large welded structures, including ships and offshore structures, are normally in operation under cyclic fatigue loadings. These structures include many geometric discontinuities, as well as material discontinuities due to weld joints. The fatigue strength at these hot spots is very important for the structural performance. In the past, various Non Destructive Evaluation (NDE) techniques have been developed to detect fatigue cracks and to estimate their location and size. However, an important limitation of most of the existing NDE methods is that they are off line; the normal operation of the structure has to be interrupted, and the device often has to be disassembled. This study explores the development of a structural health monitoring system, with a special interest in applying the technique to welded structural members in ship and offshore structures. In particular, the impedance based structural health monitoring technique that employs the coupling effect of piezoceramic (PZT) materials and structures is investigated.