用於輔助工業零件辨識之尺寸估算系統

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姓名

程劭予(Shao-Yu Cheng) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

用於輔助工業零件辨識之尺寸估算系統
(A Size Estimation Mechanism for Assisting Industrial Component Recognition)

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摘要(中)

隨著科技的日新月異，許多技術逐漸應用於工業領域，帶來了第四次工業革命，亦稱為工業4.0。其中，人工智慧(Artificial Intelligence, AI)與擴增實境(Augmented Reality, AR)的結合，為工業帶來了許多的優勢。在現代工廠中，技術人員在組裝零件時，可以即時透過AR眼鏡來查看零件的辨識結果，這需要高度準確的模型來支持。然而，僅依賴於零件的外觀和特徵進行辨識，在面對外觀相似但尺寸有差異的零件時，模型容易產生混淆，導致模型準確度下降。此外，零件的不同角度、照明條件和背景等外在環境因素也會影響模型的準確度。為了解決這一問題，我們開發了一套系統，該系統要求在外在環境干擾較低的情況下，利用擁有深度感測器的HoloLens 2來取得單一零件的RGB影像及深度影像，深度影像能提供感測器與物體之間的距離資訊。然後利用這些影像並透過我們所提出的方法來找出能容納零件的最小矩形，進而估算出其實際寬度與長度，並將我們的方法與K-means聚類演算法進行比較，該演算法也能找出容納物體的最小矩形並進行尺寸估算。找出最小矩形的目的是為了能夠統一各種形狀的零件，提供一個標準化的尺寸參考，減少計算複雜度。最後，再使用我們所估算出的尺寸資訊來對模型的辨識結果進行額外的處理。實驗結果表明，我們所提出的方法在尺寸估算上明顯優於K-means聚類演算法，並證明了模型在尺寸資訊的輔助下，能有效提升其準確度。

摘要(英)

With the rapid advancement of technology, many innovations have gradually been applied to the industrial sector, ushering in the Fourth Industrial Revolution, also known as Industry 4.0. Among these innovations, the combination of Artificial Intelligence (AI) and Augmented Reality (AR) has brought numerous advantages to the industry. In modern factories, technicians can use AR glasses to view real-time identification results of components during assembly, which requires highly accurate models. However, relying solely on the appearance and features of components can lead to confusion when dealing with components that look similar but differ in size, resulting in decreased model accuracy. Additionally, factors such as different angles, lighting conditions, and backgrounds can also affect the model′s accuracy. To address this issue, we developed a system that, under conditions of minimal environmental interference, uses the HoloLens 2 equipped with a depth sensor to capture RGB and depth images of the individual component. The depth images provide information on the distance between the sensor and the object. Using these images, our proposed method identifies the minimum rectangle that can enclose the component, estimates its actual width and length, and compares our method with the K-means clustering algorithm, which also identifies the minimum enclosing rectangle and estimates dimensions. The purpose of finding the minimum rectangle is to standardize the dimensions of components of various shapes, reducing computational complexity. Finally, we use the size information we estimated to further process the model′s recognition results. Experimental results show that our proposed method significantly outperforms the K-means clustering algorithm in size estimation, and demonstrates that the model can effectively improve its accuracy with the assistance of size information.

關鍵字(中)

★ 擴增實境
★ HoloLens 2
★ 深度影像
★ 尺寸估算

關鍵字(英)

★ Augmented Reality
★ HoloLens 2
★ Depth Image
★ Size Estimation

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 論文架構 3
二、背景知識 4
2-1 HoloLens 2 4
2-2 Unity 6
2-3 HoloLens 2 Sensor Streaming 6
2-4 MRTK3 7
2-5 OpenCV 7
三、相關研究 8
3-1 深度攝影機在理解物體大小中的應用與重要性 8
3-2 透過深度影像找出容納物體之矩形並進行尺寸估算 8
四、系統架構與流程 10
4-1 系統架構 10
4-2 系統流程 11
五、估算物體尺寸之方法 14
5-1 容納物體之最小矩形 14
5-1-1 灰階轉換 14
5-1-2 高斯模糊 15
5-1-3 邊緣偵測 17
5-1-4 膨脹與侵蝕操作 19
5-1-5 找出容納物體之最小矩形 21
5-2 座標轉換 23
5-3 尺寸估算 30
六、實驗與結果 34
6-1 實驗環境與設置 34
6-2 尺寸估算方法之比較 35
6-3 模型準確度之比較 41
七、結論與未來研究方向 45
參考文獻 46

參考文獻

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指導教授

王尉任

審核日期

2024-7-24

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