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• W3204751003 abstract "Abstract All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection." @default.
• W3204751003 created "2021-10-11" @default.
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• W3204751003 date "2021-09-01" @default.
• W3204751003 modified "2023-10-16" @default.
• W3204751003 title "Automatic Target Recognition of Millimeter-Wave Radar Based on Deep Learning" @default.
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• W3204751003 doi "https://doi.org/10.1088/1742-6596/2031/1/012031" @default.
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