Research On Imaging Optimization And Depth Information Extraction On Steel Plate Surface Defects Based On Image Processing

In this dissertation, aiming at the problems of imaging optimization and the depth-feature information extraction in the technology of steel plate surface defects detection based on machine vision, with improving the image quality as the starting point, the interaction effect laws and related theories have been researched which of the steel plate production process, the surface optical properties and to optimum imaging of surface defects. And the key techniques of steel plate surface depth extraction were achieved based on the methods of stereo vision to enhance the performance of the steel surface inspection system based on machine vision and provide a theoretical basis, and widened the ideas or techniques of automatic steel plate's surface inspection system. The main works and achievements of the thesis are as follows:1) Building an accurate surface illumination model for machine vision to describe the light scattering distribution of steel plate surface. The model is based on steel production process and surface optical properties and combined with (Bidirectional Reflectance Distribution Function) BRDF lighting model theory. Through the experiments light scattering characteristics of different steel plate surface, the rules of relationship between the light incidence angle, surface roughness and laws of light scattering under a particular light-source conditions were found. The results showed that there was an apparent specular reflection peak on steel surface, and surface light scattering was influenced greatly by light incidence angle and surface roughness, of the law of exponential distribution functions. Thus the improved semi-empirical light scattering mathematical model which based on roughness factor and surface Gaussian distribution of micro-plane components has been formed, and through non-linear model fitting and optimization to determine the parameters.2) On the basis of analysis and study on many samples of steel plate typical surface defects, aimed at the optimization problem of imaging optical pattern for steel plate surface defects, we designed the imaging experiment scheme and development the special experiment equipment to research that quantifies the effects of optical imaging parameters to the surface defects images. Further comprehensive evaluation system of image quality based on the images'multi-features was proposed to obtain the optimal imaging program for every defect. Furthermore, through the optimization imaging process model of steel plate surface defects each sub-imaging schemes were analyzed, and the portfolio optimization imaging system design scheme was obtained. As the theories research platform of steel surface imaging for defects, the model provided a basis to improve the performance of the detection system.3) In view of achieving three-dimensional depth information extraction using Stereo Vision on the industrial applications, firstly researched the camera nonlinear calibration technology. Building the nonlinear camera pinhole models for area scan CCD camera and line scan CCD camera based on the LENS distortion model, determined the exterior parameters and interior parameters of camera calibration. Using HALCON planar calibration board which was circular targets of two-way array and functions library improved the calibration method for single camera based on principle of two-step and achieved it. And the camera calibration method of binocular stereo vision system which was composed by the structure of two area scan cameras was studied, as an example achieved the calibration process. The accuracy of these parameters and stability of the algorithm were validated through experiments and accuracy analysis. The calibration method is flexible and good portability to be effectively used in industrial machine vision systems.4) For the other key technologies of stereo vision, we researched the stereo matching algorithm. The regional matching methods based on gray correlation can generate the dense disparity map. Therefore to improve the algorithm accuracy, the regional matching algorithm based on gray correlation based on normalized cross-correlation (NCC) as the similarity measure function was realized to perform the stereo matching for examples of steel plate, and combined with the requirements of steel surface depth extraction, algorithm parameters were analyzed to be optimized. Further a region-based hierarchical matching algorithm based on the mean image pyramid was proposed combining the NCC method. The algorithm accuracy and computational efficiency have been controlled efficiently through comprehensive setup of the parameters such as matching window size, the smallest eigenvalue, and the disparity search space, similarity measure threshold and others.5) On the basis of conclusions in this research, according to the detection indicators of experimental system, we analysis the imaging parameters and design the imaging system, equipment selection, installation and commissioning. A machine vision-based experimental system of the steel plate surface inspection was built with the establishment of the industrial conditions. The system consists of three parts: small strip transmission equipment, machine vision imaging system, platform of image acquisition and processing. The detection test system is an important basis of research on theoretical and experiment.