Algorithm research on image processing for crack identification of round wood

2020 ◽  
Author(s):  
Xiaogang Dang ◽  
Xiaofeng Bai ◽  
Xulang Chen ◽  
Lu Han ◽  
Lei Wang ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Identification ◽  
Round Wood

scholarly journals Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2052 ◽  
Author(s):  
Hyunjun Kim ◽  
Junhwa Lee ◽  
Eunjong Ahn ◽  
Soojin Cho ◽  
Myoungsu Shin ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Identification ◽  
Concrete Crack

scholarly journals Construction of Concrete Surface Crack Recognition Model Based on Digital Image Processing Technology

2021 ◽  
Vol 2074 (1) ◽  
pp. 012067
Author(s):  
Yuzhong Kang ◽  
Aimin Yu ◽  
Wenquan Zeng
Keyword(s):  
Image Processing ◽  
Prestressed Concrete ◽  
Crack Width ◽  
Crack Detection ◽  
Concrete Surface ◽  
Concrete Bridges ◽  
Crack Identification ◽  
Detection Accuracy ◽  
Depth Analysis ◽  
Class B

Abstract In this paper, the bridge crack detection method based on digital images is studied. In-depth analysis and evaluation are performed on the image processing algorithms such as image graying, resolution of checkerboard corner pixel rate, filtering denoising, and edge detection, etc. The calculation and software system for bridge crack width based on videos (or images) is implemented, and 15 bridge crack images are used to verify its crack detection accuracy. The results suggest that the proposed crack identification method in this paper can be used for the crack detection of reinforced concrete bridges and class B prestressed concrete bridges properly. When the crack width is greater than 0.3 mm, the calculated crack width value based on images is very close to the measured value.

Research on Crack Detection Algorithm of Mining Car Baffle

2020 ◽  
Vol 13 ◽  
Author(s):  
Weiwei Li ◽  
Fanlei Yan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Low Cost ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Crack Identification ◽  
Actual Length ◽  
Computer Vision Technology ◽  
Sobel Edge Detection

Introduction: Image processing technology is widely used for crack detection. This technology is to build a data acquisition system and use computer vision technology for image analysis. Because of its simplicity in the processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has low cost. Method: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform.Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform, and uses the improved Gamma transform to enhance the image. Result: We can conclude that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable. Discussion: To compare the performance of a new crack detection method with existing methods, are used. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification. Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs preprocessing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning

2018 ◽  
Vol 18 (3) ◽  
pp. 725-738 ◽  
Author(s):  
Hyunjun Kim ◽  
Eunjong Ahn ◽  
Myoungsu Shin ◽  
Sung-Han Sim
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Concrete Structures ◽  
Concrete Surface ◽  
Crack Identification ◽  
Speeded Up Robust Features ◽  
Structural Durability ◽  
Training Stage ◽  
Candidate Regions ◽  
Surface Images

In concrete structures, surface cracks are important indicators of structural durability and serviceability. Generally, concrete cracks are visually monitored by inspectors who record crack information such as the existence, location, and width. Manual visual inspection is often considered ineffective in terms of cost, safety, assessment accuracy, and reliability. Digital image processing has been introduced to more accurately obtain crack information from images. A critical challenge is to automatically identify cracks from an image containing actual cracks and crack-like noise patterns (e.g. dark shadows, stains, lumps, and holes), which are often seen in concrete structures. This article presents a methodology for identifying concrete cracks using machine learning. The method helps in determining the existence and location of cracks from surface images. The proposed approach is particularly designed for classifying cracks and noncrack noise patterns that are otherwise difficult to distinguish using existing image processing algorithms. In the training stage of the proposed approach, image binarization is used to extract crack candidate regions; subsequently, classification models are constructed based on speeded-up robust features and convolutional neural network. The obtained crack identification methods are quantitatively and qualitatively compared using new concrete surface images containing cracks and noncracks.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

Recent Applications of High Resolution Electron Microscopy to Crystalline Arrays of Virus Particles

1978 ◽  
Vol 36 (3) ◽  
pp. 470-482 ◽  
Author(s):  
R.W. Horne
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Analytical Techniques ◽  
Staining Technique ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Full Potential ◽  
Virus Particles ◽  
Resolution Electron ◽  
Wide Range

The technique of surrounding virus particles with a neutralised electron dense stain was described at the Fourth International Congress on Electron Microscopy, Berlin 1958 (see Home & Brenner, 1960, p. 625). For many years the negative staining technique in one form or another, has been applied to a wide range of biological materials. However, the full potential of the method has only recently been explored following the development and applications of optical diffraction and computer image analytical techniques to electron micrographs (cf. De Hosier & Klug, 1968; Markham 1968; Crowther et al., 1970; Home & Markham, 1973; Klug & Berger, 1974; Crowther & Klug, 1975). These image processing procedures have allowed a more precise and quantitative approach to be made concerning the interpretation, measurement and reconstruction of repeating features in certain biological systems.

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