fov視野的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包

fov視野進入發燒排行的影片

利用輕巧卷積神經網路之機器視覺於自動化紡織瑕疵辨識與定位

為了解決fov視野的問題，作者劉紹宇 這樣論述：

摘要............................................................iAbstract.......................................................ii誌謝...........................................................iii目錄...........................................................iv圖目錄.........................................

................vii表目錄.........................................................ix第1章 緒論......................................................11.1 研究背景與動機.............................................11.2 文獻回顧...................................................31.3 論文架構.....................

..............................4第2章 相關技術與原理..............................................52.1 深度學習...................................................52.1.1 發展歷史...................................................52.2 類神經網路（Neural network） ...............................82.2.1 類神經網路架構......

.......................................82.2.2 激勵函數（Activation function）............................112.2.3 神經網路運作流程...........................................142.3 卷積神經網路（Convolutional neural network, CNN）...........162.3.1 卷積層（Convolutional layer）..............................162.3.2 線性整流層（Recti

fied linear units layer, ReLU layer）.....182.3.3 池化層（Pooling layer）....................................192.3.4 全連接層（Fully connected layer）..........................202.3.5 歸一化指數函數（Softmax function）..........................212.3.6 卷積神經網路總結............................................212.4 資料增強（Dat

a augmentation）...............................21第3章 瑕疵檢測方法................................................233.1 光學架構....................................................233.1.1 線掃描式相機...............................................233.1.2 視野範圍（FOV）............................................253.

1.3 線掃描頻率（Line scan rate）...............................263.1.4 影像傳輸介面...............................................263.1.5 編碼器.....................................................263.1.6 光源......................................................273.2 瑕疵檢測模型........................................

........283.2.1 分割網路（Segmentation network S）.........................293.2.2 分類網路（Classification network C）.......................313.3 分割指標（Segmentation metrics）............................333.4 分類指標（Classification metrics）..........................353.4.1 二元分類（Binary case）...................

..................353.4.2 多分類（Multiclass case）...................................38第4章 實驗結果與討論...............................................394.1 實驗架構與流程...............................................394.1.1 實驗架構....................................................394.1.2 實驗流程.................

...................................404.2 實驗結果.....................................................424.2.1 二元分類....................................................444.2.2 多分類......................................................474.3 模型通用性驗證................................................50第5章

結論與未來展望................................................545.1 結論.........................................................545.2 未來展望.....................................................54參考文獻..........................................................55