用於偵測線材顏色排列之自動光學檢測系統

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姓名

鄭淳升(Chun-Sheng Cheng) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

用於偵測線材顏色排列之自動光學檢測系統
(Automatic Optical Inspection System for Wire Color Sequence Detection)

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至系統瀏覽論文 (2024-8-31以後開放)

摘要(中)

由於當今社會對於線材的巨大需求，線材的品質尤為重要。為了控制所生產線材的品質，企業對自動光學檢測（AOI）技術有著極大的渴望。AOI是一種以機械感測設備代替人眼作為檢測的手段，通過機械臂模擬人工操作來達到高速、高精確度的光學圖像檢測系統。在現有的方法中，往往犧牲運算速度來實現高精度，如卷積神經網絡（CNNs）近年來在檢測、識別和分類方面取得了不錯的效果，但仍然存在運算速度慢、資源消耗過大的問題。在本文中提出了一種用於偵測線材顏色排列之自動光學檢測系統，並將重點著重在設計出高速之線材顏色順序感測器，可以自動適應於不同種類之線材和辨識情境，如單根線材只有一種顏色、一根或兩根線材被鋁箔包覆等，而為了使線材在畫面露出較短且兩條線材彼此靠近的情況下也能順利辨識，通過邊緣檢測和形態學處理來計算線材的水平梯度，並通過一系列判別機制識別出畫面中線材的類型和顏色順序。實驗結果顯示此方法可以在保持良好運算速度的同時維持穩定的精確度。

摘要(英)

In view of the huge demand for wire in today‘s society, the quality of wire is especially required. In order to control the quality of the produced wire, the industry has a great desire for automated optical inspection (AOI) technology. AOI is a high-speed and highly accurate optical image inspection system that uses mechanical sensing equipment to replace the human eye as the inspection method, and simulates manual operation by means of a robotic arm. In the existing methods, the speed of computation is often sacrificed to achieve high accuracy. For example, convolutional neural networks (CNNs) have shown good results in detection, recognition and classification in recent years, but they still have the problems of slow computing speed and excessive resource consumption. In this paper, a high performance algorithm for the automated optical inspection of wire color sequence was proposed. This paper focuses on the design of a high-speed wire color sequence detection that can automatically adapt to different kinds of wires and recognition situations, such as single wire with only one color, and one or two wires covered with aluminum foil. In order to be able to inspect successfully even if the wire is short in the screen and the two wires are close to each other, we calculate the horizontal gradient of the wires by edge detection and morphological calculation, and identify the types and color sequences of the wires in the screen by a series of discriminative mechanisms. Experimental results show that this method can achieve a good accuracy while maintaining a good computation speed.

關鍵字(中)

★ 自動光學檢測
★ 顏色識別
★ 電腦視覺

關鍵字(英)

★ Automated optical inspection
★ Color recognition
★ Computer vision

論文目次

中文摘要 I
Abstract II
目錄 III
表目錄 IV
圖目錄 V
一、序論 1
1-1 研究背景 1
1-2 研究動機 3
1-3 論文架構 6
二、文獻探討 7
2-1 基於卷積神經網路之方法 7
2-2 基於傳統特徵擷取之方法 10
三、提出方法概述 12
四、系統架構 15
4-1 特徵擷取 16
4-2 顏色識別 25
五、實驗結果 27
5-1 辨識環境與資料集 27
5-2 實驗成果 31
六、結論 37
參考文獻 38

參考文獻

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指導教授

蔡宗漢(Tsung-Han Tsai)

審核日期

2022-6-28

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