臺灣博碩士論文加值系統

檢測產品是一個重複性高且枯燥乏味的工作，雖然利用人工檢測能改善產品的品質，但相當耗時。此工作總是依賴著個人的經驗、判斷力及專注力，沒有一定的準則，而基於自動化光學檢測(automatic optical inspection, AOI)的檢測方式來開發出一套電腦視覺檢測系統是不可或缺的。本研究使用影像處理技術、類神經網路(artificial neural networks, ANN)與基因演算法(genetic algorithm, GA)應用在自動光學檢測上。本文分為兩部分，一部分是類神經網路在ITO (indium tin oxide)導電玻璃的瑕疵分類模擬之應用，此方法用於辨識出ITO導電玻璃的瑕疵種類以及分析特徵值對於此類神經網路的分類作探討。ITO玻璃常見瑕疵現象有刮痕、裂縫、異物與粉塵等問題，針對上述瑕疵影像的外觀與比例關係為依據，我們擬定四種輸入特徵值，分別為瑕疵亮度平均值、瑕疵標準差、瑕疵平均面積與瑕疵平均周長。此四種瑕疵特徵值為類神經網路的輸入，透過類神經網路的訓練後再將測試的特徵值輸入，能準確分類出瑕疵，達到分類的目的。另一部分為基因演算法於影像強化在抗反射(anti-reflection, AR)玻璃之應用，在AR玻璃瑕疵種類中，Mura是一種由於亮度不均所造成各種低對比的瑕疵，而Mura這種瑕疵因為較難以人眼與機器直接辨識，所以針對Mura進行影像強化，透過直方圖處理後用基因演算法來調整影像，來達到凸顯Mura瑕疵的目的。

Product inspection is a high repetitive and tedious work. The use of manual inspection can improve the quality of the products, but it is greatly time-consuming. This work is always dependent on personal experience, judgment, and concentration. There are no definite standards. It is essential to develop a computer vision inspection system which is usually referred to as automatic optical inspection (AOI). This study applies image processing techniques, artificial neural network (ANN), and genetic algorithm (GA) to detect flaws of products. This thesis is divided into two parts. One is the application of neural network to classify the defects on the indium tin oxide (ITO) conductive glass. This method extract features for ANN in order to identify the defect types on ITO conductive glass. The common defects on ITO glass are scratch, crack, foreign objects and dust etc. The features are based on the appearance of the types of defects. There are four features selected: average brightness, standard deviation, the average area, and the average length of the defect. These four defect features are the inputs of the neural network. Another part is the application of genetic algorithm to enhance the contrast defects on anti-reflection (AR) glass. One of the AR glass defects is Mura which is a kind of low-contrast defect caused by uneven brightness. It’s difficult to identify Mura by human eyes and machines directly. Therefore we enhance the Mura defects before further processing. The proposed genetic algorithm using histogram information can highlight the Mura defects effectively.

圖目錄II
表目錄IV
摘要V
ABSTRACT VI
第1章 研究動機與文獻回顧1
第2章 影像處理相關技術3
2.1 直方圖處理3
2.2 Sobel邊緣強化4
2.3 二值化影像分割4
2.4 連接區塊標記法5
第3章 類神經網路7
3.1 簡介7
3.2 倒傳遞演算法9
第4章 類神經網路於瑕疵分類12
4.1 特徵擷取13
4.2 瑕疵分類流程14
4.3 實驗結果16
第5章 基因演算法19
5.1 染色體編碼型式20
5.2 選擇20
5.3 交配22
5.4 突變23
第6章 使用基因演算法於影像強化25
6.1 定義染色體25
6.2 定義適應函數26
6.3 基於基因演算法的影像強化流程27
6.4 實驗結果29
第7章 結論40
參考文獻41
圖1.1 玻璃穿透之比較圖2
圖2.1 影像使用直方圖等化進行強化4
圖2.2 連接區塊標記法掃瞄順序圖5
圖2.3 標記結果圖6
圖3.1 神經網路示意圖7
圖3.2 類神經網路架構圖8
圖4.1 瑕疵類型12
圖4.2 標準影像14
圖4.3 檢測影像14
圖4.4 二值化影像14
圖4.5 連接區塊標記影像14
圖4.6 邊緣強化(Sobel)影像14
圖4.7 分類訓練流程圖15
圖4.8 分類流程圖15
圖4.9 刮痕類型檢測圖17
圖4.10 裂縫類型檢測圖17
圖4.11 異物類型檢測圖18
圖4.12 粉塵類型檢測圖18
圖5.1 基因演算法之流程圖19
圖5.2 輪盤法21
圖5.3 單點交配22
圖5.4 兩點交配22
圖5.5 均勻交配23
圖5.6 單點突變23
圖5.7 均勻突變24
圖6.1 強化示意圖25
圖6.2 影像強化於基因演算法之流程圖28
圖6.3 檢測圖1之強化影像與直方圖(影像大小：82*82)29
圖6.4 檢測圖2之強化影像與直方圖(影像大小：166*166)30
圖6.5 檢測圖3之強化影像與直方圖(影像大小：96*96)31
圖6.6 檢測圖4之強化影像與直方圖(影像大小：77*77)31
圖6.7 檢測圖5之強化影像與直方圖(影像大小：81*81)32
圖6.8 檢測圖6之強化影像與直方圖(影像大小：95*95)32
圖6.9 檢測圖7之強化影像與直方圖(影像大小：96*96)33
圖6.10 檢測圖8之強化影像與直方圖(影像大小：115*115)34
圖6.11 檢測圖9之強化影像與直方圖(影像大小：72*72)34
圖6.12 檢測圖10之強化影像與直方圖(影像大小：139*139)35
圖6.13 檢測圖11之強化影像與直方圖(影像大小：119*119)36
圖6.14 檢測圖12之強化影像與直方圖(影像大小：108*108)36
圖6.15 檢測圖13之強化影像與直方圖(影像大小：120*120)37
圖6.16 Couple圖之強化影像與直方圖(影像大小：256*256)38
圖6.17 Army圖之強化影像與直方圖(影像大小：256*256)38
圖6.18 F16圖之強化影像與直方圖(影像大小：256*256)39
圖6.19 Lena圖之強化影像與直方圖(影像大小：256*256)39
表4.1 類神經網路的參數設定16
表4.2 各種瑕疵分類輸出結果17
表4.3 瑕疵分類之測試結果18
表5.1 參數轉換成染色體20

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