Defect detection method based on polarization structured light imaging and improved Mask R-CNN

Abstract

In order to solve the problems of imperfect surface defect detection information, low precision, low efficiency and the like, the invention provides a defect detection method based on a polarization structured light imaging technology and an improved Mask RCNN. The method comprises the following steps: firstly, combining polarization processing with structured light three-dimensional imaging to obtain a high-definition two-dimensional physical graph and three-dimensional space information of an object; performing median filtering processing on the two-dimensional physical graph; secondly, on the basis of a Mask RCNN target recognition method, adding a K-means algorithm to carry out clustering analysis on a training set, adding branches with side edge connection from top to bottom to an original FPN structure, and combining lower-layer high-resolution features and upper-layer high-resolution features to generate a new feature map; detecting an image with defects by utilizing the improved Mask RCNN network, and classifying, positioning and segmenting the defects; finally, obtaining a series of information such as the type, position, length, width, depth and area of the defect throughdata arrangement, achieving quantification of defect data, and the object surface defect detection precision and efficiency are effectively improved.

Description

technical field [0001] The invention relates to a defect detection method based on polarization structured light imaging technology and improved Mask R-CNN, belonging to the technical field of image processing. Background technique [0002] Non-destructive testing technology is to detect the defects existing in the tested object by using the changes of heat, sound, light, electricity, magnetic and other reactions without damaging or affecting the performance and internal organization of the tested object. As product quality requirements become higher and higher, detection methods become more and more abundant, and the identification of product surface defects becomes more and more stringent. Structured light imaging detection technology has applications in many aspects, and can detect three-dimensional size information of product surface defects, but in the process of three-dimensional reconstruction, due to the large area of ​​flare will affect the extraction of grating str...

AI Technical Summary

Problems solved by technology

[0003] The present invention provides a defect detection method based on polarized structured light imaging technology and improved Mask R-CNN for the problems of imperfect information, low precision and low efficiency of surface defect detection at present.

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