Research On Non-Defective Information Filtering In Surface Defect Inspection Of Cold Steel Strip

Though online surface inspection systems for cold rolled strips have been widely applied, some critical problems still need further research, such as insufficient real time processing, low defect recognization, etc. Plenty of defects samples of cold rolled strips were collected, and intensive study has been conducted with regard to the problems above. According, some new methods were proposed to settle these problems. All the technologies have been proved by experiments and would be valuable for industry application.Main works and achievements of the thesis are as follows:(1) A distributed detection system scheme based on Client/Server mode was used according to the analyses of technical requirements and application features for cold steel strip surface defect inspection. It has effectively solved the problem of multi-cameras image processing and image data transmission. A new image collecting method based on multithread processing and image ring queue was presented. This new method can separate image collecting thread from image processing thread and make them process simultaneously. Therefore the problem of image data accumulation was effectively solved and system collapse due to highly used memory was also avoided. In this case, the memory used stabilized at about 528MB and CPU utilization ratio stabilized at about 14%. The system operates stably and reliably.(2) General principles to evaluate rolled strips images were established which is quite useful to the following image processing. It is of great importance for reducing the error of defect recognition.(3) A new fast defect area detection algorithm using multivariate discriminant function was put forward, which takes full account of both defect uncertainty and image texture background. This algorithm makes it possible to only process the defective image in the following processing so that the amount of data processing will be reduced. In the experiment, the defect detection ratio can be up to 99.5%, and the misjudge ratio and omission ratio are separately 0.5% and 1.75%.(4) A neighborhood impulse noise evaluation was introduced. Image noise evaluation was added to this new algorithm, which was helpful to keep the image details and avoid blurring after removing the noise. Compared with classic filters, the new method performs effectively in noise detection accuracy, distortion measuring and vision effect.(5) A new filter based on wavelet transform and nonlinear anisotropic diffusion was proposed. With the advantage of both wavelet transform and nonlinear anisotropic diffusion, this new filter basically eliminated the effects of image texture background. Thus, clear and accurate outlines can be acquired, which is quite valuable to the following image auto-segmentation and other processing. At the same time, JPEG lossy compression was conducted to the images filtered, and the average compression rate is 2.3 times as that of the image without filtering, which saves more storage space for the image processing, saving and transmitting.