Method Of Strip Surface Defect Image Detection And Classification

There are two points that making the online strip surface defect detection greatly important on steel production and quality control. One point is,with the high speed development of iron and steel industry, the strip production line speed is increasing; another is consumption enterprises on the strip also put forward higher surface quality requirements with their increasing quality requirements in many filed. But steel strip surface defect detection is slow development with the rapid development of image processing technology. The reason of this problem is failure to make more improvements of related technology for production needs and failure to change these technologies for overcome the obstacles form production environment.The overview and research of this paper listed as follows are in order to further improve the scientific detection that including accurate data of Industrial production standardization, to improve the degree of automation, to classification accuracy which related rework cost and maintenance cost, to reduce the trade disputes and keep the enterprise reputation.(1)The developments of domestic and foreign steel surface defect detection technology are reviewed, and analyzed the strip surface defect machine vision inspection technology future trends.(2)Studied for strip surface image acquisition ways with a variety of production environments. The research topics include lighting, CCD sensors and detection methods. And then the Strip surface defect detection position and detection method in many different production environments have been identified.(3)Studied for the main factors affecting the cold/hot rolling strip surface defect detection and classification results, which including morphology, formation mechanism, location distribution.According to the research, three further questions are raised.(1) The question of strip boundary detection under conditions of non-uniform distribution of gray in strip surface image. non-uniform distribution of gray is the common status of strip surface image. Therefore the accuracy of boundary detection results will seriously affect the boundary defect rate of the strip.(2)The question of the classification with no-fixed shape defect images. Most pseudo-defects have no-fixed shape, and many features of defect image also have no-fixed shape. If not consider the non-fixed shape characteristics of defects, the defect rate will not adapt the industrial level.(3)The issues of the strip image classification method have not enough capability in practice.This paper has done the following in-depth research in many image detection and classification methods with these problem:(1)Studied for the typical image detection and classification method in strip surface defect detection and classification.(2)Developed an adaptive boundary detection method which based on Gaussian. This method can detection the gray differences with the surface and the background, can dynamically determine the interference range of gray. Compared to the traditional method of strip surface defect detection, this method can detect stripe boundary that contains the boundary interference. The accuracy of the method up to62.8%form4.6%and1.6%in the most severe case. Detection speed is every thousand lines of1.82seconds(3)In the stage of defect segmentation, developed an Region of Interest algorithm that base on standard strip gray image. This algorithm has the advantages of simple structure, and through the discrimination threshold parameters can be adjusted for search results. Developed an defect image Connected Component marking and merging method by controlling the4parameters can be resized results.In the stage of defect classification, developed double-limited and supervise-connect Isomap dimensionality reduction and classification method (dls-Isomap). Based on the dimensionality reduction technique from Isomap, the connection of neighborhood graph is limited by key parameters k-nn and ε-radius, and inter-class neighborhood points are connected extensionally with the supervision of class labels. According to multi-classes roll-swiss data experiments, all the points can be embedded in lower dimensions with the complete inter-class and intra-class geometric structure, and the "short circuit" in the Isomap can be solved by the dls-Isomap method. In addition, stripe surface defect images data experiments show that the new proposed classification method is suitable for the classification of stripe surface defects including more no-fixed shape defect images, with the recognition rate of78%for cold-roll strip images, and93%for hot-roll strip images with water.(4)Developed a new image classification system, which including a set of image class subjective evaluation mechanism, two sets of the main classifier and a set of three image classification framework. The experiment result show that the filtering error amount of defects in the total number is less than1%. The overall recognition rate of all the experimental steel coil up to90%.The research results have been successfully applied in Jiangsu Shagang Group Co. Ltd. in 1450Hot Rolling Product Unit. The economic benefits of these application about three million yuan per year.