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研究生: 余立安
Yu, Li-An
論文名稱: 基於機器視覺與次像素邊緣偵測於LED探針之自動化檢測系統
Auto-inspected System for LED Probes Based on Machine Vision and Sub-pixel Edge Detection
指導教授: 蘇崇彥
Su, Chung-Yen
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 65
中文關鍵詞: 機器視覺次像素邊緣偵測自動化檢測影像處理
英文關鍵詞: machine vision, sub-pixel edge detection, autonatic inspection, image processing
DOI URL: https://doi.org/10.6345/NTNU202202688
論文種類: 學術論文
相關次數: 點閱:85下載:14
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  • LED (light-emitting diode,發光二極體)從原先做為電子裝置的指示燈使用,如今已被廣泛的應用在工作或一般用途的照明上;而LED需要由LED探針做燈泡特性的量測,以篩選出LED的好壞,因此越來越多LED探針被大量地生產。本論文的研究目的就是提出一套自動的光學檢測系統,以提升LED探針的品質,提高生產效率,降低不良品的產出,並能夠即時回饋產品的資訊,而檢測的流程則使用許多機器視覺與影像處理的技術,包含運用Otsu門檻值搭配Canny的邊緣檢測得到初步的邊緣位置、次像素邊緣檢測取得更精確的邊緣位置、以及物件分群等方法,以提升量測的準確度,最後可以控制探針角度誤差在1%而半徑誤差在2%左右,並且提出一套能夠精準的區分出有瑕疵探針的方法,實驗證明本論文所提出的方法能夠快速且精準的分析LED探針的尺寸以及好壞。

    In the beginning, LEDs (light-emitting diode) were used as indicator lamps for electronic devices, and nowadays LEDs have been widely utilized in general lighting devices[1]. To test the quality of LEDs, more and more LED probes are required. In this paper, the efficiency of autonatic optical inspection system has been proposed. The proposed process is able to ensure high-quality of LED probes, improve the efficiency of production, reduce the output of defective products and feedback information of products in time. The detection framework use wide image processing, consists of Otsu threshold and Canny edge detection to get coarse edge, sub-pixel edge detection, object extraction, i.e. The proposed method can quickly and accurately analyze the size of LED probes whose angle error about 1% and radius error about 2%. The experimental results verify the effectiveness of our methods.

    摘 要 I ABSTRACT II 目 錄 IV 圖 目 錄 VI 表 目 錄 IX 第一章 緒論 - 1 - 1.1 研究背景 - 1 - 1.2 研究動機 - 1 - 1.3 研究目的 - 3 - 1.4 論文架構 - 3 - 第二章 文獻探討 - 4 - 2.1 自動化檢測相關文獻 - 4 - 2.2 次像素邊緣偵測 - 6 - 2.2.1 曲線配適 (Curve-fitting) - 7 - 2.2.2 局部區域效應法 (Partial area effect) - 9 - 2.2.3 重建法 (Reconstruction) - 12 - 2.3 OTSU演算法介紹 - 15 - 第三章 檢測系統架構 - 17 - 3.1 硬體設備 - 17 - 3.2 開發環境 - 20 - 3.3 影像處理流程 - 20 - 3.3.1 影像前處理 - 21 - 3.3.2 初步邊緣偵測 - 22 - 3.3.3 物件分群 - 28 - 3.3.4 第一階段瑕疵檢測 - 29 - 3.3.5 次像素邊緣偵測 - 31 - 3.3.6 計算角度與半徑 - 32 - 3.3.7 第二階段瑕疵檢測 - 34 - 3.4 自動化檢測系統與流程 - 35 - 3.4.1 自動化流程架構圖 - 35 - 3.4.2 使用者介面設計 - 38 - 第四章 實驗結果與分析 - 43 - 4.1 LED探針瑕疵分析 - 43 - 4.2 執行時間 - 45 - 4.3 靜態量測分析 - 49 - 4.4 自動化量測分析 - 50 - 第五章 結論與未來展望 - 53 - 參考文獻 - 54 - 附  錄 - 58 - 自  傳 - 63 - 學 術 成 就 - 64 -

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