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• W4319431032 abstract "With the development of network technology, image processing has been combined with the depth of the Internet of things to learn, and other technology plays an important role in anomaly detection. But ordinary visual sensor features are simple, mostly to transmit the image to the cloud, not only can't found the abnormal situation in time, and because the data for long-distance transmission and increasing network bandwidth leads to network delay Disasters and other situations are destructive and sudden. If not discovered in time, irreparable losses will be caused. Therefore, how to reduce the amount of data transmission and network delay in abnormal detection has become a big challenge In this paper, an anomaly detection model based on edge-cloud is proposed. Firstly, the anomaly is detected on the edge end, the anomaly image is extracted and compressed, and the anomaly is classified in the cloud. To achieve this, first of all, it is proposed to build an anomaly primary detection model on the edge end, and the data collected by the vision sensor is detected as abnormal. Then a data reduction method on the edge end is proposed, and the abnormal image is feature-extracted to reduce data transmission. Finally, the features of the transmission are fine-classified using a detailed classification of an anomaly on the cloud. A large number of experimental results show that this method can realize the anomaly detection technology without affecting the recognition accuracy and reducing the data transmission." @default.
• W4319431032 created "2023-02-09" @default.
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• W4319431032 date "2022-10-01" @default.
• W4319431032 modified "2023-10-16" @default.
• W4319431032 title "Edge-Cloud Hybrid Tiny Data Reduction Model for Anomaly Detection" @default.
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• W4319431032 doi "https://doi.org/10.1109/icebe55470.2022.00019" @default.
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