本研究成功開發出一套機能型衣物標籤的光學檢測系統。此系統硬體包含馬達驅動的運輸平台，以馬達帶動滾筒達到傳輸標籤之用途，並搭配影像擷取模組，同時探討光源對檢測影響，進而選擇所需的光源。檢測程式根據標籤被判定為瑕疵的原因進行撰寫，其中包括變形、缺陷、劃線記號等。對於變形與缺陷標籤，系統使用參考相減法進行檢測，以人眼判斷合格的標籤作為標準片，將測得的待測標籤中心點與標準片中心重疊後，兩者影像相減，並將影像區分成前端、尾端、全部檢測，從剩餘像素值進行判斷，若標籤變形嚴重，中心點位置會相差甚遠，剩餘像素值則會愈大，若超出各區域的判斷閥值時，便能有效地判定瑕疵。劃線記號的瑕疵標籤在白光照射下，不易顯現劃線部位，故嘗試使用不同色光進行照射，其中以藍色光最為適當，然而在標籤尖端處微小的劃線部位仍然不易顯示，故在區域部位撰寫不同影像處理演算法進行檢測。實作中使用包含正常與瑕疵且經過品管人員確認的樣本測試，以評估開發出的檢測系統之檢測能力。

This study successfully developed an optical detection system for functional clothing labels. The system hardware includes a motor-driven transport platform with an image capture module. Also explore the impact of the light source on the detection, and then select the desired light source. The software is written based on the reason the label is judged to be defective, including deformation, defects, and underline marks. For deformation and defect labels, the system uses the reference subtraction method for detection. A label that is judged to be qualified by the human eye as a standard film. Then, overlapping and subtracting the standard label and the label to be tested. Divide the image into front end, tail end, and all detections, and judge from the remaining pixel values. If the label is severely deformed, the position of the center point will be far apart, and the remaining pixel values will be larger. When the judgment threshold of each area is exceeded, the defect can be effectively determined. The label of the underline mark is not easily visible under the illumination of white light. Therefore, it is attempted to use different color lights for illumination, and the experimental results are most appropriate for blue light. However, the small underline mark at the tip of the label is still difficult to display, so different image processing algorithms are written for detection in the area. Finally, samples including normal and defected parts have been tested to verify the capability of the inspection system developed.

[1] Taiwan Textile Federatio 紡拓會，https://www.textiles.org.tw/TTF/main/home/Home.aspx
[2] Lin, Y. C., et al. (2014). "Position determination of a ball grid array by automated optical inspection method. " 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, IEEE-NEMS 2014 (pp. 97-101).
[3] Karlekar, V. V., et al. (2015). "Fabric Defect Detection Using Wavelet Filter. " 2015 International Conference on Computing Communication Control and Automation: 712-715
[4] Win, M., et al. (2015). "A Contrast Adjustment Thresholding Method for Surface Defect Detection Based on Mesoscopy." IEEE Transactions on Industrial Informatics 11(3): 642-649.
[5] 王龍華，「高反光物體影像處理技術研究」，國立雲林科技大學電子工程系，碩士論文，2018
[6] 范光照，廖偉博，陳智遠，陳守恒，周睿程，「環形粉末冶金件之自動光學檢測」國立台灣大學機械工程學系，期刊論文，2008
[7] 林松濱，「裸晶粒自動化視覺檢測模組之開發」，國立台灣科技大學機械工程系，碩士論文，2011
[8] Guanjun, B., et al. (2019). "Cracked Egg Recognition Based on Machine Vision." Computers and Electronics in Agriculture 158: 159-166.
[9] Hosseininia, S. J., et al. (2016). "Flexible Automation in Porcelain Edge Polishing Using Machine Vision." Procedia Technology 22: 562-569.
[10] Sabzi, S., et al. (2017). "Machine Vision System for the Automatic Segmentation of Plants Under Different Lighting Conditions." Biosystems Engineering 161: 157-173.
[11] Chang, W.-T., et al. (2013). "Preliminary Development of an Automated Optical Inspection System for Quality Control of Circular Saw Inserts." International Journal of Automation and Smart Technology 3(1): 9-17.
[12] Lai, S.-H. and M. Fang (2002). "A Hybrid Image Alignment System for Fast and Precise Pattern Localization." Real-Time Imaging 8(1): 23-33.
[13] 林彥邦，「回流管接頭瑕疵之光學檢測」，國立台灣科技大學機械工程系，碩士論文，2013
[14] 蘇培加，「鐵蛋缺陷檢測」，國立台灣科技大學機械工程系，碩士論文，2010
[15] 林義祥，「機器視覺應用於照相手機鏡片模組邊緣瑕疵檢測」，國立交通大學管理學院碩士班，碩士論文，2012
[16] 郭智揚，「油路控制閥光學檢測系統之開發」，國立台灣科技大學機械工程系，碩士論文，2010
[17] Chethan, Y. D., et al. (2018). "Machined Surface Monitoring in Turning Using Histogram Analysis by Machine Vision." Materials Today: Proceedings 5(2): 7775-7781.
[18] Huang, B., et al. (2018). "Research and Implementation of Machine Vision Technologies for Empty Bottle Inspection Systems." Engineering Science and Technology, an International Journal 21(1): 159-169.
[19] H. Zeljko., et al. "Improved Canny Edge Detector in Ceramic Tiles Defect Detection." IEEE Industrial Electronics, IECON 2006 -32nd Annual Conference, pp. 3328-3331.
[20] Borsu, V., et al. (2010). "Automated Surface Deformations Detection and Marking on Automotive Body Panels." Proceedings of the 6th IEEE International Conference on Automation Science and Engineering, Toronto, Ontario, Canada.
[21] Xu, Y., et al. (2013). "Research on the Image Acquisition and Camera Control of Machine Vision Camera Based on LabVIEW." 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics: 499-502.
[22] Wang, L., et al. (2013). "Detection of Fruit Skin Defects Using Machine Vision System." 2013 Sixth International Conference on Business Intelligence and Financial Engineering: 44-48.
[23] 林俊男，「一種加強影像邊緣銳化的影像放大方法」，元智大學資訊工程系，碩士論文，2002
[24] 黃梓閔，「板型渦簧自動化光學檢測系統之研發」，國立台灣科技大學機械工程系，碩士論文，2014
[25] 張鎧麟，「渦簧與扭簧自動化光學檢測系統之改良研究」，國立台灣科技大學機械工程系，碩士論文，2015
[26] 謝宗明，「自動化影像定位系統及其定位標記之應用」，國立成功大學製造工程研究所，碩士論文，2005
[27] 吳鎮宇，「金屬元件光學檢測系統之開發」，國立台灣科技大學機械工程系，碩士論文，2017
[28] 朱漢祥，「拉鍊自動化光學即時檢測系統之開發」，國立台灣科技大學機械工程系，碩士論文，2013
[29] Zouxy09專欄https://blog.csdn.net/zouxy09/article/details/49080029
[30] The Imaging Source兆鎂新，https://www.theimagingsource.tw/
[31] 美商國家儀器台灣分公司，http://www.ni.com/zh-tw.html
[32] 鍾國亮，「影像處理與電腦視覺」，東華書局，2015。
[33] 琦勝企業有限公司，http://www.conch.com.tw/