| dc.description.abstract | 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. | en_US |