論文閱讀筆記《Unsupervised fabric defect detection based on a deep convolutional GAN》
核心思想 本文提出一種基於DCGAN的無監督紡織物缺陷檢測算法，本文還是延續了利用DCGAN根據缺陷圖像重構無缺陷的圖像樣本，然後再尋找重構圖像和缺陷圖像之間的差別，差別超過閾值的判定爲缺陷像素，但在實現的細節上還是有很多的創新，本文的訓練和測試流程如下圖所示 圖(a)展示了訓練過程，首先是對DCGAN網絡進行訓練，生成器 G G G輸入的是一維的噪聲向量，輸出的是32*32大小的圖塊 x
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相關文章
- 1. 論文閱讀筆記《A Surface Defect Detection Method Based on Positive Samples》
- 2. Summary of defect detection algorithms based on deep learning
- 3. 論文閱讀筆記《Segmentation-based deep-learning approach for surface-defect detection》
- 4. 論文閱讀筆記《Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Net》
- 5. 論文閱讀筆記《A weak supervision machine vision detection method based on artificial defect simulation》
- 6. 論文閱讀- Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
- 7. 論文閱讀筆記《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》
- 8. A Surface Defect Detection Method Based on Positive Samples
- 9. 論文閱讀筆記《A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection》
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