Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing

arXiv:2404.07405v1 Announce Type: new
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