Objective

2

Steel plate is an indispensable raw material in machinery manufacturing

automobile production

aerospace

and shipyard manufacturing. Its surface quality directly affects the performance and quality of final products. In an actual steel plate production line

various defects often occur on the surface of the steel sheet due to various processing factors

such as imperfect processing technology and aging of equipment components. Owing to the variety of surface defects and the complex gray structure

if the existing image segmentation technology is applied to a steel plate defect image with a complex gray structure and blurred target edge

then low recognition efficiency

obvious over-segmentation

and other issues will arise. In this study

we analyze the spatial characteristics of the gray matrix of a steel plate defect image and find that transforming a two-dimensional image gray matrix into 3D space can help us identify the defect position of the steel plate surface. Therefore

we propose a steel surface defect recognition algorithm based on the 3D gray matrix.

Method

2

First

the 2D gray image of the steel plate's surface defect image is obtained and transformed into a 3D gray matrix using the matrix transformation algorithm. The transformed matrix is drawn in a 3D space

and the local concave and convex portions are identified

that is

the position of the defect corresponding to the surface of the steel sheet. Second

the kriging space interpolation algorithm is used to process the transformed 3D matrix. The kriging algorithm is derived from geostatistics; therefore

to make the method suitable for processing image data

this study introduces semi-class variance to improve the Kriging interpolation algorithm. A contour map of the 3D gray matrix is thus drawn. Third

after obtaining the contours corresponding to the 3D matrix

we use the position principle between the points and curves to construct a topological relationship tree of the contours and determine the inclusion relationship between the contours. Finally

according to the combination of customized global and local search strategies

the local concave and convex areas are searched to locate the defect area and achieve the purpose of dividing the surface defects of the steel plate.

Result

2

Our method can effectively identify the surface defect area of the steel plate

is not sensitive to illumination change

and improves the effective division rate under the premise of ensuring a low error rate. Fisher's threshold segmentation method (FT)

classical CV model (CV)

HTB model (HTB)

and improved background difference method (ABDM) are compared. For the FT algorithm based on edge information and the Fisher criterion

a fixed single threshold is selected

which can effectively segment an image with a large defect area and obvious difference with background grayscale. However

distinguishing the surface defect of the steel plate effectively is difficult when the target is similar to the grayscale of the background. For the CV model

image segmentation with a complex structure is inaccurate

and the number of iterations is large. The method of processing the four types of defect images has over-segmentation. This method cannot cope with the image segmentation task under boundary blur conditions. The HTB model solves the problem that the CV model cannot

that is

to adapt to the grayscale uneven image segmentation problem. However

regardless of the good segmentation effect on the knotted defect image

the algorithm has a poor segmentation effect on the other three types of defects. The ABDM algorithm

which aims to improve the background difference

requires that the constructed background model be as suitable as possible for the foreground image; therefore

the segmentation effect of the steel plate defect image that has a large defect target region is poor. In this study

the 3D gray matrix of the image is used to realize localization and segmentation of the defect region. The proposed algorithm can obtain accurate segmentation results for defect target extraction of the four types of defect images. Compared with Otsu

1DMFE

and MFEE in terms of segmentation of the hole and roll image

our method improves the effective information rate by 1.6% and 2.1%

respectively

under the premise of ensuring that the mis-segmentation rate is kept below 2.0%. For the inclusion image

our method has an effective information rate of more than 85% under the premise of 3.4% mis-segmentation rate.

Conclusion

2

By transforming a 2D grayscale image into a 3D gray matrix

we analyze the characteristics of the grayscale image in 3D space and find that the defect area on the surface of the steel plate image corresponds to the local unevenness of the 3D space. Thus

a method based on an image segmentation algorithm for steel plate surface defects in a 3D gray matrix is proposed. First

a 3D gray matrix is generated from the original gray image. Second

a gray contour map is drawn. Third

the relationship between contours is determined

and a contour topological relationship tree is constructed. Finally

the contour is traversed according to a search strategy

the local concave and convex regions are identified

and the target position is determined to achieve segmentation. Experimental results show that the proposed algorithm can effectively segment common steel plate defects with uneven grayscale. From qualitative and quantitative perspectives

the algorithm can quickly and effectively extract defect regions in a steel plate image. In summary

the proposed steel plate defect image recognition algorithm based on a 3D gray matrix can effectively identify many types of steel plate defects

even in image recognition with a complex defect structure.