Surface Defects Detection of Wipers Based on Machine Vision
Aiming at the common typical surface defects of wiper,such as scratches,spots(pits and sand holes),a method of wiper surface defect detection based on machine vision is proposed.Firstly,according to the surface image of the wiper collected by CCD,the image morphology processing and Gabor filtering method are used to suppress the texture noise of the metal frosting by designing specific structural elements of image preprocessing,and the gamma gray image enhancement algorithm is used to enhance the contrast between the wiper defect and the background.Then,the image maximum entropy threshold defect is used to segment the surface defects of the wiper.Finally,the feature extraction and classification of typical defects on the surface of the metal wiper shell are realized,and the size of the relevant defects is calculated.The experimental results show that the detection speed of the pro-posed method for typical surface defects of wiper in industrial production reaches an average of 0.724 s/piece,which has good detec-tion accuracy and can be well adapted to the industrial environment.
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