Abstract
Detection of scratch defects on randomly textured surfaces remains challenging due to their unnoticeable visual features. In this paper, an algorithm for piezoelectric ceramic plate surface scratch defects based on the combination of fuzzy c-means clustering and morphological features is proposed. Foreground membership of each gray value is calculated firstly on a reference set of training images by fuzzy c-means clustering and the interpolation method, then an enhanced image is obtained by multiplying the foreground membership function and gray image. The location relationship between regions and the gradient direction of regions is extracted from the binary image of the enhanced image. Based on the morphological features, isolated non-scratched defects are filtered out and the intermittent scratches are merged. Experiments show that the algorithm can be used to detect scratch defects on the surface of a piezoelectric ceramics plate with randomly textured surfaces.
Highlights
Detection of piezoelectric ceramic plate surface defects is essential to maintain performance in a range of applications
In order to enhance the quality of images of surface scratch defects, the length of the strip light source is twice as long as that of the ceramic chip, and the strip light source irradiates the ceramic plate at a low angle
An algorithm is proposed for the detection of scratch defects on a piezoelectric ceramic plate surface based on a combination of the fuzzy c-means (FCM) method and morphological characteristics
Summary
Detection of piezoelectric ceramic plate surface defects is essential to maintain performance in a range of applications. In order to enhance the quality of images of surface scratch defects, the length of the strip light source is twice as long as that of the ceramic chip, and the strip light source irradiates the ceramic plate at a low angle. A new detection method utilizing the fuzzy c-means (FCM) algorithm and morphological characteristics is presented to extract piezoelectric ceramic plate surface scratches. In the method proposed in this paper, FCM was used to cluster the Algorithm foreground and background of the ceramic image offline, and the foreground membership degree of the gray value was calculated. Let = ( , , ⋯ , )′ be th used to enhance the ceramic image, the scratch was segmented according to the morphological analysis method This provides a new method for surface scratch detection on piezoelectric ceramics.
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