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Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing
April 12, 2024, 4:45 a.m. | Jaemin Kang, Hoeseok Yang, Hyungshin Kim
cs.CV updates on arXiv.org arxiv.org
Abstract: Deep learning has been successfully applied to object detection from remotely sensed images. Images are typically processed on the ground rather than on-board due to the computation power of the ground system. Such offloaded processing causes delays in acquiring target mission information, which hinders its application to real-time use cases. For on-device object detection, researches have been conducted on designing efficient detectors or model compression to reduce inference latency. However, highly accurate two-stage detectors still …
abstract application arxiv board computation cs.cv deep learning detection detectors images inference information mission object power processing sensing simplifying stage type
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