Concrete bridge crack detection by image processing technique by using the improved OTSU method

Vivekananthan, V.; Vignesh, R.; Vasanthaseelan, S.; Joel, E.; K, Sunil Kumar

doi:10.1016/j.matpr.2022.11.356

Cited by 17 publications

(6 citation statements)

References 8 publications

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“…As shown in figure 6, comparing the method proposed in this paper with traditional binary image processing methods such as global threshold, Otsu threshold [22], and adaptive threshold [23], it can be clearly seen that the method proposed in this paper is able to extract all the bright fringes connectivity domains, which provides more data volume for the subsequent calculation of the filament diameter with higher accuracy. Finally, according to equation (7), the extraction of the center of gravity of bright fringes as shown in figure 7 can be obtained.…”

Section: A Segmentation Mask Window-based Diffraction Fringe Center O...mentioning

confidence: 92%

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

Liu,

Gan,

Liao

et al. 2023

Meas. Sci. Technol.

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As chemical fiber filaments diameter variation directly affects spinning quality, spinning condition monitoring becomes more and more important in an intelligent manufacturing environment. Firstly, based on the single-slit Fraunhofer diffraction model and the Babinet principle, the non-contact filaments diffraction model is developed to generate diffraction fringe patterns by irradiating chemical fiber filaments with He-Ne laser. Secondly, for the characteristics of diffraction fringe patterns, a segmentation mask window-based diffraction fringe center of gravity extraction method is proposed, and a zero-phase Butterworth low-pass filter is designed and applied to the diffraction light intensity curves to accurately extract the extreme points positions of the adjacent dark fringe and calculate the diameter information. Finally, the non-contact chemical fiber filaments appearance diameter online inspection experiment platform is built to carry out static and dynamic experiments of filaments. The experimental results show that the maximum absolute value of absolute error is less than 1.1 μm for static measurement and less than 10 μm for dynamic measurement.

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Section: A Segmentation Mask Window-based Diffraction Fringe Center O...mentioning

confidence: 92%

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

Liu,

Gan,

Liao

et al. 2023

Meas. Sci. Technol.

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“…Figure 22b shows the pixels on the upper edge of the crack. The upper edge of the crack corresponds to the number of rows A(k + 1) and A(k − 1) in Column k + 1 and Column k − 1, respectively, and the calculation for the inclination angle θ 1 corresponding to the upper edge of the pixel crack in Column k is as shown in Equations ( 7) and (8).…”

Section: Calculation Of Actual Crack Widthmentioning

confidence: 99%

“…Vivekananthan et al used the grayscale discriminant method to preprocess the image, set the threshold using the Otsu algorithm, and used the Sobel filter to detect cracks on the edges of the image pixel. Their results showed that the accuracy of this crack detection method was up to 95% [8]. Li et al described an innovative vision-based pavement crack detection strategy.…”

Section: Introductionmentioning

confidence: 99%

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li,

Yin,

Zhang

2023

Sensors

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Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

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“…Due to the influence of the experimental environment, we have to use some simple image processing algorithms [23]. Firstly, the collected images are filtered by the median filtering algorithms to reduce the influence of diffraction, and then image noise points are removed by the OTSU method [24][25][26]. The obtained image centroid data and linear fitting relationship are shown in Figure 10.…”

Section: Verification Of the Linear Relationshipmentioning

confidence: 99%

Rectangular Amplitude Mask-Based Auto-Focus Method with a Large Range and High Precision for a Micro-LED Wafer Defects Detection System

He,

Ma,

Wang

2023

Sensors

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Auto-focus technology plays an important role in the Micro-LED wafer defects detection system. How to accurately measure the defocus amount and the defocus direction of the Micro-LED wafer sample in a large linear range is one of the keys to realizing wafer defects detection. In this paper, a large range and high-precision auto-focus method based on a rectangular amplitude mask is proposed. A rectangular amplitude mask without a long edge is used to modulate the shape of the incident laser beams so that the spot shape distribution of the reflected laser beam on the sensor changes with the defocus amount of the wafer sample. By calculating the shape of the light spots, the defocus amount and the defocus direction can be obtained at the same time. The experimental results show that under the 20× microscopy objective, the linear range of the auto-focus system is 480 μm and the accuracy can reach 1 μm. It can be seen that the automatic focusing method proposed in this paper has the advantages of large linear range, high accuracy, and compact structure, which can meet the requirements of the Micro-LED wafer defects detection equipment.

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Concrete bridge crack detection by image processing technique by using the improved OTSU method

Cited by 17 publications

References 8 publications

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Rectangular Amplitude Mask-Based Auto-Focus Method with a Large Range and High Precision for a Micro-LED Wafer Defects Detection System

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