April 2, 2024, 7:43 p.m. | Tianyang Li, Chao Wang, Hong Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.01074v1 Announce Type: cross
Abstract: Detecting transmission towers from synthetic aperture radar (SAR) images remains a challenging task due to the comparatively small size and side-looking geometry, with background clutter interference frequently hindering tower identification. A large number of interfering signals superimposes the return signal from the tower. We found that localizing or prompting positions of power transmission towers is beneficial to address this obstacle. Based on this revelation, this paper introduces prompt learning into the oriented object detector (P2Det) …

abstract arxiv cs.cv cs.lg detection geometry identification images interference power prompt prompt learning radar resolution signal small synthetic type

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