[1]MA Ling,LU Yue,JIANG Huiqin,et al.An automatic small sample learning-based detection method for LCD product defects[J].CAAI Transactions on Intelligent Systems,2020,15(3):560-567.[doi:10.11992/tis.201904020]
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An automatic small sample learning-based detection method for LCD product defects
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 3
Page number:
560-567
Column:
学术论文—机器学习
Public date:
2020-05-05
- Title:
- An automatic small sample learning-based detection method for LCD product defects
- Keywords:
- defect diagnostics; target classification; deep learning; small sample learning; convolution neural network; transfer learning; DCGAN; continue learning
- CLC:
- TP391
- DOI:
- 10.11992/tis.201904020
- Abstract:
- Aiming at the demands of high-resolution liquid crystal display (LCD) product quality online inspection, we propose an automatic deep learning-based detection method for LCD defects. An LCD surface defect detection model is constructed based on small sample learning by designing an adaptive shallow feature extraction layer and introducing a sparse convolution structure to extract the multi-dimensional and multi-scale deep features. Furthermore, the model is trained and enhanced using transfer learning and a deep convolutional generative adversarial network (DCGAN) for data expansion, and the LCD surface defect detection model is built based on small sample learning. The original high-resolution image is segmented into sub-images suitable for convolutional neural network (CNN) learning by designing automatic segmentation and location pretreatment software. The detection results of multi-type defects according to the category and location coordinates of the classification model’s output are obtained. The experimental results show that the model can effectively improve the detection rate and reduce the missed detection rate.
- References:
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