| dc.description.abstract | 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. | en_US |