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• W4207023301 abstract "<div>In this correspondence, an efficient YOLOv3-based approach for DoA estimation named YOLO-DoA is proposed, which is implemented as a detection task to spatially separated angular boxes. DoAs of sources with confidence scores are directly predicted from the spectrum proxy with YOLO-DoA and an end-to-end estimation is realized. By combining squeeze and-excitation operation, cross stage partial connections, and an improved loss for angular box regression, the performance of YOLO-DoA is significantly enhanced.</div>" @default.
• W4207023301 created "2022-01-26" @default.
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• W4207023301 date "2022-01-25" @default.
• W4207023301 modified "2023-09-28" @default.
• W4207023301 title "YOLO-DoA: A lightweight, real-time, and effective implementation for DoA estimation" @default.
• W4207023301 doi "https://doi.org/10.36227/techrxiv.18737684.v1" @default.
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