Research On Low-texture Image Stitching For The Surface Defect Location Of Large Metal Component

In industrial production,the defect detection of workpiece surface is an important part of quality control.In modern mechanical manufacturing industry,defect detection is often carried out by means of machine vision.For the detection of small defects on the surface of large components,due to the small field of vision of industrial cameras with high resolution,multiple groups of industrial cameras are often used for regional image acquisition and defect detection.Regional defect detection helps to improve the detection accuracy,but the defect detection results are difficult to quickly correspond to the workpiece surface,which is not conducive to the rapid location of defects in the production process and the analysis of the causes of subsequent defects.After the completion of regional defect detection,it is of great significance for the production process to splice multiple areas into complete workpieces by image stitching technology,and display the defect detection results on the complete workpiece.However,the low texture of the metal component surface makes it difficult to apply the traditional image stitching technology.At the same time,the phenomenon of uneven brightness on the metal workpiece surface caused by the complex lighting in the industrial production environment also brings difficulties to the stitching process.In order to solve the above problems,this paper studies the low texture image stitching technology of large metal components for surface defect location under the environment of industrial production.The specific content includes:The first chapter summarizes the research status of metal surface color enhancement,sub-pixel feature extraction and low texture image stitching at home and abroad,expounds the research background and significance of this paper,and introduces the research content and overall framework of this paper.In the second chapter,a non-contact marking method is designed for metal low texture surface features,and the color equalization algorithm is adaptively improved.On this basis,a comprehensive metal surface feature extraction method based on color equalization and feature enhancement is proposed,and the accurate extraction of metal weak texture surface features is realized.In the third chapter,the sub-pixel precision extraction of the marked center point is realized by improving the centerline extraction algorithm.Then the accurate matching between the marked points is completed based on the improved multi-scale normalized cross-correlation method.Based on the principle of perspective transformation,the optimal design of the number and arrangement of marked points is realized,and the image registration is completed,and the effectiveness of the marking point setting rule proposed in this paper is verified by experiments.In chapter 4,an equalization preprocessing method with excessive brightness difference on the surface of the registered image is proposed,and then an improved hierarchical smooth transition fusion method for multi-resolution fusion is proposed.On this basis,several common fusion methods are compared by introducing the evaluation index which is more suitable for industrial scenes,and the effectiveness of the fusion method is verified.Finally,the global location conversion for local detection defects is completed based on the complete mosaic image.In the fifth chapter,based on the method proposed in this paper,the low texture image stitching and defect location system of large metal components is developed.The system includes several functional modules: tag extraction,marker point matching,registration fusion and defect location.The system is used to realize the stitching and defect location conversion of several large weak texture components.The sixth chapter summarizes the research results and conclusions of the weak texture surface splicing method and defect location conversion of large metal components,analyzes the shortcomings of this paper,and looks forward to the further research in the future.