研究生: |
陳文化 Wen-hua Chen |
---|---|
論文名稱: |
印花織物瑕疵檢測系統 Defect Detection System for Rrinted Fabrics |
指導教授: |
郭中豐
Chung-Feng Kuo |
口試委員: |
黃昌群
Chang-Chiun Huang 高志遠 Richard Kao 邱錦勳 Chin-hsun Chiu |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 自動化及控制研究所 Graduate Institute of Automation and Control |
論文出版年: | 2011 |
畢業學年度: | 99 |
語文別: | 中文 |
論文頁數: | 81 |
中文關鍵詞: | 影像處理 、色彩空間 、模糊理論 、小波轉換 |
外文關鍵詞: | Image process, Color space, Fuzzy theory, Wavelet transform |
相關次數: | 點閱:348 下載:0 |
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印花織物是一種高附加價值的紡織品,具有豐富的色彩及多變化的圖案,是其它織物所不能及,惟雖然日常生活中使用的大都是彩色織物,但目前應用於織物的影像分析技術仍只對灰階織物。本研究之目的即在發展印花織物瑕疵自動檢測系統,以檢測印花織物數位影像並找尋其中瑕疵的影像,提高印花織物產品之品質。影像來源由掃描器將印花織物轉成數位影像資料,取得重覆圖樣色彩空間(Color Space)的紅綠藍(RGB)值,統計XY軸RGB累加平均值,比較XY軸上的RGB累加平均值可得到最小重覆區域並儲存。以最小重覆區域影像RGB累加平均值當作印花織物數位影像比較的依據,利用小波轉換去檢測輸入印花織物的數位影像是否有瑕疵,最後取得具瑕疵的影像時,統計瑕疵影像的RGB累加平均值與最小重覆區域的RGB累加平均值,經比對後系統會顯示有瑕疪區域的影像。最後比較最小重覆影像與瑕疪區域的影像可得到瑕疵影像部位,利用模糊分類器檢測瑕疪種類。由實驗結果顯示,此系統可分析出96.8%的印花織物瑕疵的種類,對印花織物自動檢驗具有很好成效。
Printed fabric is a high value-added textile that has rich colors and variable patterns. Although colored fabrics are commonly used in daily life, image analysis technology for fabrics is limited to gray-scale fabrics. This study aimed to develop an automatic defect detection system for printed fabrics that detects the digital images of printed fabrics and identifies the defected images, in order to enhance the quality of printed fabric products. The images are scanned from printed fabrics and converted to digital data. After obtaining the RGB values of repeated Color Space, the RGB accumulative average mean of XY axles are calculated and compared to obtain and save the minimum repeating zone. The RGB accumulative average mean of the minimum repeating zone is used as the basis of comparison for digital images of printed fabrics, and wavelet transform is used to detect the flaws in the digital images of printed fabrics. After the flaws are identified, the RGB accumulative average mean of the flawed images and minimum repeating zone are calculated and compared to indicate the images of the flawed zone. Lastly, the images of the minimum repeating zone and defect image are compared using fuzzy classifier to detect the type of defect. The experimental results showed that this system can analyze 96.8% of the detect types of printed fabrics, and has practical values in automatic detection of the quality of printed fabrics.
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