多視角影像下的工業產品辨識：可增量學習的卷積神經網路模型;Industrial Product Recognition in Multi-view Images:A Convolutional Neural Network Model with Incremental Learning Capability

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/94551

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94551

Title:	多視角影像下的工業產品辨識：可增量學習的卷積神經網路模型;Industrial Product Recognition in Multi-view Images:A Convolutional Neural Network Model with Incremental Learning Capability
Authors:	賴聖鎧;Lai, Sheng-Kai
Contributors:	工業管理研究所
Keywords:	工業產品辨識;三維物體識別;遷移學習;增量學習;卷積神經網路;Industrial product recognition;Three-dimensional object recognition;Transfer learning;Incremental learning;Convolutional neural networks
Date:	2024-07-16
Issue Date:	2024-10-09 15:14:58 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究針對工業產品識別的需求，提出了一套方法，包含以下三個部分：(1)新型多視角卷積神經網路模型，(2)三維物體的遷移學習，(3)增量學習的演算法。由於工廠環境下取得的資料容易有翻轉、背景雜亂等問題，傳統的二維影像辨識方法效果不佳，本研究提出了一種新的網路架構，稱為自注意力殘差網路，具有優秀的背景分割能力和全局特徵理解，可幫助多視角的二維圖像進行三維物體識別。另一個問題是在現實中取得足以訓練好模型的資料量需要極高的成本，需要對多視角卷積神經網路進行遷移學習以提升辨識效果。此外，本研究針對無法儲存過去資料的情境之類別增量學習，提出局部深度模型融合。實驗表明，使用自注意力殘差卷積神經網路作為特徵擷取層，性能大幅領先目前最先進的卷積神經網路，並透過多視角卷積神經網路之遷移學習，在缺乏訓練資料的情形下進一步提高準確率。本研究提出的局部深度模型融合，對比其他增量學習演算法同樣取得較佳的效果。最後我們使用真實的工業產品拍攝，模擬在實際辨識情況會遇到的困境，並演示模型之效果。;This study addresses the need for industrial product identification by proposing a method that includes the following three components: (1) a novel multi-view convolutional neural network model, (2) transfer learning for three-dimensional objects, and (3) an incremental learning algorithm. Traditional 2D image recognition methods perform poorly due to issues like flipping and cluttered backgrounds commonly found in factory environments. This study introduces a new network architecture, called the “SARNet”, which excels in background segmentation and global feature comprehension, aiding in the recognition of 3D objects from multi-view 2D images. Another challenge is the high cost of obtaining sufficient data to train a model effectively in real-world scenarios, which necessitates the use of transfer learning for the multi-view convolutional neural network to enhance recognition performance. Additionally, this study proposes “Partial Deep Model Consolidation” for class incremental learning scenarios where storing past data is not feasible. Experiments demonstrate that using a “SARNet” as the feature extraction layer significantly outperforms the current state-of-the-art convolutional neural networks. Transfer learning with the multi-view convolutional neural network further improves accuracy in situations with limited training data. The proposed “Partial Deep Model Consolidation” also achieves better results compared to other incremental learning algorithms. Finally, we use real industrial product photographs to simulate the challenges encountered in actual recognition scenarios and demonstrate the effectiveness of the model.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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