用於3D物體辨識基於視圖的注意力圖卷積監督式對比學習神經網路

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/94561

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94561

Title:	用於3D物體辨識基於視圖的注意力圖卷積監督式對比學習神經網路
Authors:	涂珮涓;Tu, Pei-Chuan
Contributors:	工業管理研究所
Keywords:	工業自動化;多視圖三維物體辨識;注意力機制;對比學習;automated industry;multi-view 3D object recognition;attention mechanism;contrastive learning
Date:	2024-07-22
Issue Date:	2024-10-09 15:16:38 (UTC+8)
Publisher:	國立中央大學
Abstract:	工業革命自18、19世紀興起，歐美國家透過機器取代手工生產，演進出四次工業革命而目前正處於第四次。本研究針對工業革命的核心自動化，以提高生產效率、降低成本、提升品質為目標，特別關注於製造業中應用的機器視覺系統。傳統三維物體辨識方法多利用二維多視角圖片，但未充分利用多視角圖片間的相關性，以及現實生活中的拍攝環境可能會影響圖片品質增加模型辨識難度。因此，本研究旨在提出一套辨識三維產品的系統，包括基於視圖的圖卷積神經網路、圖片重要特徵提取以及對比學習訓練方法。具體目標為提高辨識效能、提升對圖片重點的捕捉能力以及增強在現實生活中的穩健性。為達成此目的，本研究將採用有效聚合多視角圖片訊息的基於視圖的圖卷積神經網路、注意力機制以提取重要特徵資訊，以及監督式對比學方法來訓練神經網路以提升模型泛化能力。這些方法的詳細內容將在後續章節中詳細探討。;The Industrial Revolution emerged in the 18th and 19th centuries, during which European and American countries replaced manual labor with machines, leading to four distinct industrial revolutions, with the current era being the fourth. This study focuses on the core of the Industrial Revolution, automation, aiming to improve production efficiency, reduce costs, and enhance quality, particularly through the application of machine vision systems in the manufacturing industry. Traditional methods of three-dimensional object recognition often utilize two-dimensional multi-view images but fail to fully exploit the correlation between these images and the potential impact of real-life shooting conditions on image quality, thereby increasing the difficulty of model recognition. Therefore, this study aims to propose a system for recognizing three-dimensional products, comprising a view-based convolutional neural network, feature extraction from images, and contrastive learning training methods. The specific objectives are to improve recognition efficiency, enhance the capture of key features in images, and strengthen robustness in real-life scenarios. To achieve these goals, the study will adopt a view-based convolutional neural network that effectively aggregates information from multiple-view images, an attention mechanism to extract important feature information, and supervised contrastive learning methods to train neural networks and enhance model generalization capabilities. The detailed implementation of these methods will be discussed in subsequent chapters.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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