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研究生： 呂昊叡
Lu, Hao-Jui
論文名稱： LED背光鍵盤的自動光學檢測系統之改進
An Improved LED Keyboard Inspection System
指導教授： 韓永楷
Hon, Wing-Kai
口試委員: 李哲榮
Lee, Che-Rung
姚兆明
Yiu, Sui-Ming
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 28
中文關鍵詞： 自動光學檢測 、影像處理 、演算法應用
外文關鍵詞： AOI, Image Processing, Algorithm Application
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    • 在工業檢測的場合中，自動化影像辨識 (AOI) 技術是一種常見的 應用，現成的影像處理軟體與設備因為授權費與硬體成本問題導致 成本居高不下，並且在產線的需求發生變化時修改的彈性，且準確 度與分析速度並不滿足產線的要求，這份論文中，我們與瑞士的個 人電腦周邊設備供應商「羅技電子」合作，量身設計了新的 AOI 解 決方案，滿足公司的需求。新的解決方案是基於 OpenCV 開發而成， 利用多台視訊攝影機取代昂貴的工業相機，達到了低成本的要求。 本研究所實作出的產品檢驗流程可以用來檢測羅技電子生產的背光 鍵盤的刻字，以及燈光顏色是否符合出貨標準。

    In product inspection and quality control, automatic optical inspection (AOI) is a common application. Ready-made AOI softwares and instru- ments exist, but they come with high deployment cost and little flexibility when the requirements are changed. Moreover, the accuracy requirement or inspection time may not meet the specific need of production site. In this thesis, we cooperate with Logitech, a Swiss provider of personal computer and mobile accessories, to design tailor-made AOI solution to meet the company’s need. Our solution is developed based on OpenCV, and utilises multiple web-cams to replace expensive industrial camera, thus achieving a low cost solution. This solution is used to inspect the lettering quality and the LED color of the backlit keyboards from Logitech, so as to determine if the product meets the shipping standard or not.

    􀴡謝ii 􁐜要iii Abstract iv 1 Introduction 1 1.1 Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Preliminaries 3 2.1 Color Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Thresholding and Binarization . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Blob Detection [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Feature Point Detection [2] . . . . . . . . . . . . . . . . . . . . . . . . 7 3 Methods 9 3.1 Lettering Defect Detection . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 LED Defect Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4 Experiments 16 4.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2 Testing Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Testing Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.4 Testing in the Presence of LED Defects . . . . . . . . . . . . . . . . . 19 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5 Conclusion and Future Work 23 References 25

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    [11] A. Kumar, “Computer-vision-based fabric defect detection: A survey,” IEEE transactions on industrial electronics, vol. 55, no. 1, pp. 348–363, 2008.
    [12] M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimen- sional data,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, 2014.
    [13] H.Cantzler,“Randomsampleconsensus(ransac),”InstituteforPerception,Action and Behaviour, Division of Informatics, University of Edinburgh, 1981.

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