Abstract：Optical coherence tomography(OCT)isthe high resolution and nondestructive optical inspection method whichhas been used to evaluate the internal quality of pearls. In order to expand the range of applications of optical coherence tomography technique, an automatic detection method for internal defects of freshwater nucleated pearls by optical coherence tomography is proposed. According to the grayscale change of defect layer, the proposed method extracted the gradient feature and defect location feature of the defect region in the image, and then established the back propagation neural network(BPNN)model for defection prediction based on the extracted feature. Twenty optical coherence tomography images of defect pearls and twenty optical coherence tomography of images of health pearls were collected for image preprocessing and feature extraction in experiments, and K-means algorithm test was used to test the feature compatibility, andthe compatible features were the input of the back propagation neural network model which finally classified the defect recognition. The experimental results show that the matching degree of feature extraction is 92.5%, and the classification accuracy is up to 100%, which proved the feasibility and effectiveness of proposed method, and showed the proposed method can be used as an effective method for the identification and classification of internal defects of freshwater pearls.
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2020, Vol. 41 
