Research On Recognition And Location Of Scratch Defects On Vehicle Body Surface Based On Binocular Vision

The auto paint repair robot is an intelligent equipment used in the automobile production and maintenance industry.It integrates advanced technologies such as robots,image sensors,and automatic control.Because the auto paint repair process is more complex,a set of auto body surface defect positioning and repair has been developed.The repaired automation equipment is of great significance.This paper takes the automobile painting repair robot as the research object,focusing on the key technical problems such as target recognition,classification and positioning of the painting robot,and designing the vehicle body surface defect visual inspection system of the automobile painting repair robot,aiming at the image processing,classification and stereo of the body surface defects To carry out theoretical and experimental research on matching and positioning,the main research contents of this article are as follows:(1)Use the binocular camera as the image acquisition tool to locate the space coordinate of the center of the defect area on the surface of the car body.In response to the positioning requirements of the surface defects of the car body,the imaging model and principle of the binocular camera are studied,and the conversion relationship between the imaging coordinate systems is analyzed;then,using the checkerboard as the reference object,the system calibration is completed by the calibration method of Zhang Zhengyou,and the binocular vision is solved.The internal and external parameters of the system are calibrated stereoscopically to realize the mapping from the ideal model to the actual application.Finally,the three-dimensional matching technology is used to complete the spatial position positioning of the defect area,and the error fluctuation of the depth information in the Z-axis direction is analyzed to ensure that the error meets the painting requirements and provide positioning support for the next painting process.(2)In view of the fact that the image of car body surface defects is affected by factors such as uneven external illumination,heat generation of image acquisition equipment,resulting in a lot of noise on the car body surface image,resulting in the inability to accurately extract the effective edge information,a fusion wavelet modulus transformation and morphology are proposed Learned edge detection algorithm to extract and segment car body surface defect images.First,on the basis of traditional morphology,adjust the order of operations and optimize the morphological operator;then use wavelet transform to fuse the high and low frequency components processed by the two edge detection algorithms separately,and the low frequency components are assigned different weights according to the energy of the image.Fusion,the high-frequency component is compared with the absolute value,and the larger of the two is taken as the high-frequency component of the fused image,and the high and low frequency components are reconstructed to obtain the edge fusion result;the experiment shows that the algorithm can improve the image signal-to-noise ratio and enhance The effect of image edge continuity is obvious.(3)Aiming at the high-accuracy and high-robust recognition requirements of defect areas on the surface of the car body,a defect intelligent classification method based on feature fusion is proposed.First,the edge detection and SIFT feature extraction algorithms are used to obtain the geometric features and local texture features of the car body defect image;then,in order to merge the two features,the K-means clustering algorithm is used to build a word bag model to cluster the SIFT features.The feature fusion is completed by serial fusion and input into the SVM classifier;finally,the penalty coefficient C and kernel function parameters of the SVM are optimized by the gray wolf optimization algorithm;the experimental results show that the average classification and recognition success rate of the defect area on the surface of the car body reaches 93%.This paper studies the relevant theories and key technologies of car body surface defect recognition and positioning,proposes an overall detection plan,and designs corresponding image processing algorithms,and applies binocular stereo vision positioning technology to solve the segmentation,classification and spatial positioning of car body surface defect images With regard to related technical issues,the vehicle body surface defect detection experiment was carried out through the built-up robot binocular vision inspection experimental platform.The experiment showed that the system has good engineering practical value.