Superpixel-based workpiece surface defect segmentation method

Abstract

A superpixel-based workpiece surface defect segmentation method has the following key points in a technical scheme: performing image block division on a processed workpiece surface image to obtain m*msub-image blocks of a pixel size; calculating an energy value and an entropy value of a sub-image block gray scale co-occurrence matrix, selecting one image as a standard sample image, comparing energy values and entropy values of remaining sub-image blocks with those of the standard sample image, and obtaining defective images with through screening; utilizing a superpixel SLIC algorithm to segment the defective images into superpixels; adopting a spectral clustering NJW algorithm to cluster the superpixels to obtain a defect segmentation image; and calculating the center of gravity and theregion area of the defect, and according to the positions of sub-image blocks and the focal distance of a camera, calculating to obtain the actual specific position of the defect on the workpiece surface. The method provided the invention calculates from the superpixel level, reduces the large calculation amount based on pixels for a high-resolution large-sized image, has higher calculation efficiency, and at the same time, the interference of background texture and noise in the image are also suppressed.

Description

technical field [0001] The invention relates to a method for segmenting workpiece surface defects based on superpixels, belonging to the fields of workpiece surface quality detection and image processing. Background technique [0002] The surface quality of the workpiece has a major influence on the quality of the product or subsequent processing steps. Especially in the processing of high-precision parts, due to chips, machine tool vibration, tool wear, and clamping and handling, various defects often occur on the surface of the workpiece, resulting in a decrease in product positioning accuracy and a reduction in service life. Due to the status quo of small workpiece surface defects, strong interference on the processing site, unstable inspection quality brought about by offline manual sampling inspection, and the concept of digital factory, offline manual inspection is no longer applicable to the real situation. [0003] Therefore, the surface quality detection method bas...

AI Technical Summary

Problems solved by technology

However, due to the randomness and complexity of images, the requirements for image preprocessing are relatively high

[0004] The above methods a

Images