Abstract
Identification of the highly reflective surface defects on roller parts is a requirement to assure high quality of parts. However, the highly reflective roller surface has the reflection characteristics, which easily lead to the missed detection or wrong detection of defect targets in the visual recognition process. For this problem, a new identification method based on image library matching is proposed. First, to protect the edge information of the defect target while eliminating noise, preprocessing of the measured initial images can be accomplished by using the accelerated optimized bilateral filtering. Second, the entropy and grid gray gradient are used to achieve rough segmentation of highly reflective surface defects on roller parts. Finally, a defect fine identification method based on the Hu invariant moment matching integrated with morphological classification is proposed for achieving image library matching and further quickly removing the pseudodefects. Experimental tests were conducted to verify the effectiveness of the proposed method in achieving accurate identification of highly reflective surface defects on roller parts. The proposed method has an accuracy of 98.2%, and the running time can basically meet the requirements of real-time performance.
Highlights
Because of its reflective properties, the highly reflective rotary surface (HRRS) can clearly reflect images of the surface of smooth precision rotary parts, of which the aerospace precision bearing roller is a typical example
Because of external interference and other factors, the collected images are distorted or the defect information is blurred [8], which leads to missed detection or wrong detection. e core purpose of surface image processing of metal parts is to accurately identify the defect targets in the image. erefore, defect identification is very important for surface quality assurance of metal parts with the HRRS
The part to be tested with surface defects is first placed on the turntable and adjusted until its center is on the rotary axis of turntable. en, the illumination light generated by the collimated LED light source is incident on the total internal reflection (TIR) prism, and the incident light is reflected by the TIR prism and vertically irradiated on the digital micromirror device (DMD)
Summary
Because of its reflective properties, the highly reflective rotary surface (HRRS) can clearly reflect images of the surface of smooth precision rotary parts, of which the aerospace precision bearing roller is a typical example. E threshold weighting was used to improve the effectiveness of Mathematical Problems in Engineering the segmentation threshold It has achieved good performance in image segmentation of metal surfaces with gray peak distribution. Vasilic and Hocenski proposed a surface defect segmentation algorithm based on Canny edge detection and corner positioning [12], which can better extract defects such as scratches and pits, but the defect extraction effect for images with more complex backgrounds is not good. Mao and Zhang proposed a workpiece image object identification algorithm based on the top-hat transform sequence analysis [22], which has good antinoise and antibackground interference capabilities Most of these target identification methods based on top-hat transformation can hardly meet the integrity requirements of workpiece defect segmentation, and often there are omissions and over segmentation.
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