臺灣博碩士論文加值系統

光影效果通常會導致視覺辨識或是定位的錯誤或失敗。儘管基於ConvNet的視覺辨識定位系統對光影汙染效果顯示出了強大的適應性，但是在某些強烈的光影汙染效果下，辨識定位系統仍可能會失敗。在本研究中，我們提出了一種利用 Pix2Pix GAN的數據擴增方法，用於在各種光影汙染效果下自動生成對像圖像。在對視覺辨識定位ConvNet進行訓練時，將生成的光影效果圖像包括在內，用以豐 富訓練集，從而在強烈光影效果下具有更好的辨識及定位的性能。實驗 數 據證明，物件 座 標辨識的準確性大大提高。所提出的框架亦保持了我們所開發的「 One-Shot」 概 念，意義是用戶只需要對目標物體拍照 一次 即可完成一套對光影有強大適應性的物件座標辨識器 。

Illumination effects often result in errors or failure in visual object localization. Whereas ConvNet-based object localization frameworks have shown tremendous robustness to the illumination effect, under some strong illumination effects, the system may still fail. In this paper, the author proposes a data augmentation method utilizing Pix2Pix GAN for automatic generation of object images under various illumination effects. Upon training for the object localization ConvNet, the generated images are included to enrich the training set for a better performance under strong illumination effects. Experimental evidence shows that the accuracy of object coordinate detection can be improved significantly. The proposed framework maintains our concept of “One-Shot” where the user only needs to take a basis photo of the target object in order to create an illumination-robust object coordinate detector.

目錄
摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究目標 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 深度卷積神經網路位置辨識 6
2.2 One-Shot架構 6
2.3 生成對抗網路 8
2.3.1 傳統GAN 8
2.3.2 Pix2Pix GAN簡介 11
第三章 方法驗證實驗 12
3.1 實驗目的與規劃 12
3.1.1 拍攝光影影像 12
3.1.2 Pix2Pix GAN訓練 14
3.1.3 One-Shot訓練 16
3.2 光影污染位置辨識實驗 17
3.3 驗證標準 19
3.4 實驗結果總結 26
第四章 強烈光影生成實驗 29
4.1 實驗目的與規劃 29
4.1.1 拍攝光影影像 29
4.1.2 Pix2Pix GAN訓練 32
4.1.3 One-Shot II訓練 38
4.2 光影污染位置辨識實驗 39
4.3 定位驗證標準 43
4.4 靜態定位實驗結果 49
4.5 真實vs生成影像準確度比較 51
4.6 不同光影強度下準確度比較 52
第五章 手臂控制實驗 53
5.1 實驗設置 53
5.2 影像定位手臂控制實驗 54
5.3 驗證標準 57
5.4 實驗結果 58
第六章 結論與未來展望 63
參考文獻 64

參考文獻
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