Abstract
Steel plate surface defects seriously reduce the steel wear resistance,high temperature resistance,corrosion resistance,fatigue resistance and other properties.Therefore,the detection of plate surface defects is very important.This paper proposes a new method to detect steel defects based on machine vision.Collecting images of steel plate surface in various light conditions are discussed.Firstly,the defect images are preprocessed,and then the preprocessed images are changed to binary images and are processed morphologically.Finally,the image background and object graphics are separated,and the surface defect features are extracted to calculate the defect area and perimeter.After the calibration,the defect’s area and length can be obtained.The experimental results show that the method is of reliability and repeatability.
Published Version
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