DEEP convolutional neural network of microscopy images for pearl defects detection

You Feng; Pang Yue; Huacai Chen

doi:10.1117/12.2653335

15 December 2022 DEEP convolutional neural network of microscopy images for pearl defects detection

You Feng, Pang Yue, Huacai Chen

Proceedings Volume 12478, Thirteenth International Conference on Information Optics and Photonics (CIOP 2022); 124781I (2022) https://doi.org/10.1117/12.2653335
Event: Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), 2022, Xi'an, China

Abstract

Presently, the pearl quality inspection mainly relies on manual. In this paper, we collected the microscopy images of pearl samples and built the novel method of pearl defects detection based on deep convolutional neural network. 1. Identification and classification of pearl defect images: a) 2000 images of 250 pearls were taken by stereomicroscopy as data set; b) The data set was augmented with ImageDataGenerator toolbox; c) The impact of overfitting was reduced by combining Dropout method; d) Based on VGG-16 model, feature extraction and fine-tuning methods were adopted to achieve the ideal recognition and classification effects.2. Pearl defect area calculation: a) The image was preprocessed using MATLAB software, including color space conversion, image filtering and threshold segmentation; b) In order to obtain clear, highly differentiated and continuous contour images, the improved Sobel operator was used for edge detection; c) The pearl defect area in a single plane was obtained by inverse edge detection.

Citation Download Citation

You Feng, Pang Yue, and Huacai Chen "DEEP convolutional neural network of microscopy images for pearl defects detection", Proc. SPIE 12478, Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), 124781I (15 December 2022); https://doi.org/10.1117/12.2653335

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KEYWORDS

Data modeling

Edge detection

Convolutional neural networks

Feature extraction

Image filtering

Convolution

Image processing

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