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Deep learning
- 2. 內容大綱 應用於AOI之深度學習架構 鑽石電鍍切割線檢測 案例 高反射高爾夫球桿刮傷檢測 案例 橡膠材料缺損檢測 案例 機殼表面刮傷檢測 案例 Intelligent Automated Inspection Laboratory
- 3. 致謝 Acknowledgement 臺北科技大學 蘇詠靖 顧問、陳金聖 教授、林志哲 教授 盧柏瑄、李柏桀、盧志嘉、鄭絢陽、陳奕帆、楊福生、 蘇偉鳴 科技部、產學計畫 結合機器視覺與深度學習之表面缺陷檢測系統 鑽石磨粒切割線製程檢測與分析 深度學習之外觀瑕疵檢測系統 Cell Phone Gasket智動化檢測 Intelligent Automated Inspection Laboratory
- 4. Method of Image based Inspection Deep Learning • Regular rule based tools fail • Deformable objects • Variable orientation • Cracks inspection • Scratches inspection • Injection moulding • Heterogeneous molding • Medical imaging Rule-based processing • Code reading • Gauging • Print inspection • Fixed orientation • Dimensional measurements • Presence or absence checking • Print inspection • Rigid objects Intelligent Automated Inspection Laboratory
- 5. Deep learning 1958 1986 1990 2006 History Deep ANN Gradients vanishing Gradients exploding Local optimal • SVM • Random forest Backpropagation • CNN • RNN First artificial neutral network (ANN) by Frank Rosenblatt Deep learning • 2007 GPU computing • 2015 TensorFlow • 2016 PyTorch • 2017 Keras Figure courtesy of Andrey Kurenkov, Y. LeCun 2018 Dropout x1 x2 x3 w1 w2 w3 1 Perceptron 0 Intelligent Automated Inspection Laboratory
- 6. Data driven model Deep general specific Intelligent Automated Inspection Laboratory
- 7. Offline Training Online Prediction TrainingTrainingData Pixel Annotation Extract Image Patches DNN model DNN model Sliding Window Extraction Softmax OK Defect … Weights s Padding P Intelligent Automated Inspection Laboratory
- 8. Back-End Linux Platform Database Linux Platform Front-End Viewer Transfer learning DB AI analyzer (NTUT-Net) 0 1 0 1 0 1 0 10 1 0 1 0 1 0 1 Data driven Intelligent Automated Inspection Laboratory
- 9. 鑽石電鍍切割線檢測 案例 鎳層線徑線徑 顆粒數 Intelligent Automated Inspection Laboratory
- 10. 鑽石電鍍切割線檢測 案例 C.-C. Ho, et al., IEEE ICASI 2018 團聚 鎳鑽磨料 外徑 鎳層厚度 突出高度 琴鋼線 Intelligent Automated Inspection Laboratory
- 11. C.-C. Ho, et al., ISMTII 2019 Intelligent Automated Inspection Laboratory
- 12. 高反射高爾夫球桿刮傷檢測 案例 蘇詠靖、游原瑋、何昭慶#，科儀新知, 2018 連續孔針孔 殘膠 白霧 刮傷 擦傷 Intelligent Automated Inspection Laboratory
- 13. 相機 鏡頭 條燈 球桿 何昭慶、游原瑋，專利I644096, 2018 Intelligent Automated Inspection Laboratory
- 14. Silicone rubber: seamlessly sealing gaskets Provide the water-proofed protection • Quality control of molding silicone rubber gaskets production is very challenge • Rely on manual observation to classify the faults Challenges: 1. Dark and low reflective material surface of the silicone rubbers. 2. Defects are irregular shape. AI: • Ultimately a technology to better extract faint signal from noise. • Deep Learning is not magic, it cannot extract signal from nowhere C.-C. Ho, et al., ISPEMI 2018 Intelligent Automated Inspection Laboratory
- 15. Inner ring Deep Learning: labeling Thickness Notches Background 1 Background 4 Background 5 Background 2 Background 3 Intelligent Automated Inspection Laboratory
- 16. Stride: 2 input image size : 112×112×3 28×28×256 56×56×64 7×7×2048 7×7×1024 14×14×512 1st 2nd 4th 5th ‧‧ ‧ Output filteroutput size Classes ResNet model [7×7×2048]ꞏ[ 2048×5] weights Block Output Intelligent Automated Inspection Laboratory
- 17. Input Image weights Weighting image Feature blending Resnet_V1_50 Result Threshold Deep learning process C.-C. Ho, et al., ICATI 2019 Intelligent Automated Inspection Laboratory
- 18. Blending 原始影像 特徵圖 Mask 缺陷位置 Training Intelligent Automated Inspection Laboratory
- 19. 機殼表面刮傷檢測 案例 Intelligent Automated Inspection Laboratory input 64 × 64 64 × 64, 64 32 × 32, 256 16 × 16, 512 8 × 8, 1024 8 × 8, 2048 8 × 8, 3094 average pooling 8 × 8 (stride 8) average pooling 4 × 4 (stride 4) average pooling 2 × 2 (stride 2) [8 × 8, 64] [8 × 8, 256] [8 × 8, 512] [8 × 8, 1024] [8 × 8, 2048] concat layer
- 20. Deep Learning Challenges for AOI Tiny defect size Limited defect numbers Imbalanced data Updating framework Transferred Pattern Intelligent Automated Inspection Laboratory
- 21. Thanks for your attention Q & A Intelligent Automated Inspection Laboratory