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- W4304142023 abstract "A large number of parts are used in the production activities of the assembly plant. The quantity and quality of these parts in the process of transportation, distribution, use, and recycling in the factory have an impact on production. With large amount of parts and tools, how to provide the right types and quantity of parts at the time and right location becomes the key to business success. The traditional method uses manpower which is costly and time-delayed. Previous single sensor monitoring has its own limitations. For example, weight recognition is hard to determine when multiple type items are mixed. Visual identification is susceptible to problems such as occlusion and difficulty in identifying dense areas and every item needs to be tagged by using RFID. Therefore, this paper introduces the multi-sensor method, which uses a mathematical approach to fuse weight and visual data to predict different types and quantities of parts in small containers. This study also provides a case study, simulated classification and counting of Aircraft General Standard(AGS) parts, which are mainly fasteners. They are small in size, expensive, and difficult to identify by a single sensor. Fusion results show up to 80% in average accuracy which is an improvement over single vision(73%) and weight detection up to 50%." @default.
- W4304142023 created "2022-10-11" @default.
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- W4304142023 date "2022-09-01" @default.
- W4304142023 modified "2023-09-27" @default.
- W4304142023 title "Counting assistant through multi-sensor fusion for inventory monitoring" @default.
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- W4304142023 doi "https://doi.org/10.1109/icac55051.2022.9911172" @default.
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