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- W3178472777 abstract "Medication safety has been a important research in many decays. If there is a mistake in the process of packaging medicines, it may cause great harm to patients. This research is aimed at the development of automatic drug image verification functions. Our system is composed of two stages. The first stage is arbitrary axis-aligned object detector, which is mainly based on deep residual network and feature pyramid network(FPN). The detector predicts the rotation bounding boxes for drugs by multi-scale feature maps generated by FPN. Then, the rotation bounding boxes are axially aligned, and the drug image is cropped according to axis-aligned bounding box. The second stage is feature matcher, which is based on feature embedding network. The embedding feature extracted by feature embedding network is combined with the geometric feature obtained by arbitrary axis-aligned object detector to determine whether the drug in image is belonging to expected category that user specify. Our system can detect multiscale rotating drugs and can identify drugs at any position, angle, and placement surface. And due to the addition of geometric features, it can effectively identify drugs with similar appearance but different sizes. Finally, according to the experimental results, a high accuracy rate of 0.047% FAR can be obtained." @default.
- W3178472777 created "2021-07-19" @default.
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- W3178472777 date "2020-12-18" @default.
- W3178472777 modified "2023-10-16" @default.
- W3178472777 title "Arbitrary Axis-aligned and Multi-scale Drug Recognition System" @default.
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- W3178472777 doi "https://doi.org/10.1109/icot51877.2020.9468730" @default.
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