| 英文摘要 |
This article mainly discusses National Museum of Taiwan History (abbr. NMTH) introducing technology to assist collection inventory and develop image recognition inventory equipment in 2022. It describes the purpose and practical application of building this experimental equipment, and introduces the applied technology and design. Furthermore, it analyzes the current manual inventory process and discuss the feasibility of implementing image recognition for collection inventory at present.
According to the Inventory Procedures for Collections in Public Museums, public museums must inventory their collections. NMTH has to inventory of all collections at least once every 10 years, in accordance with the current collection size. In the end of 2022, the collection of NMTH has exceeded 140,000 pieces, causing considerable management pressure. Therefore, it is trying to build experimental collection identification models and equipment using the YOLOv5 detection algorithm to assist in collection identification and inventory, expecting to assist internal management and promote inventory. However, although identification technology is developing at a rapid pace, it still needs to be discussed whether it has really reached the expectation of reducing the burden on inventory personnel. Moreover, there are many items in the collection of NMTH that are similar in shape, such as statues of gods, cake prints, etc. Whether images identification can effectively identify individual differences is also unknown. Therefore, this article selects a series of“cake prints”from the collection of NMTH as the test objects. It aims to discuss how image recognition technology can assist in making inventory procedures smoother and more accurate when dealing with three-dimensional collections that share high similarity. Additionally, it explores whether current image recognition technology has matured to the point where it can be practically integrated into inventory processes. |
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