计算机科学
核(代数)
目标检测
跳跃式监视
人工智能
背景(考古学)
空间语境意识
代表(政治)
对象(语法)
计算机视觉
领域(数学)
遥感
模式识别(心理学)
地理
数学
考古
组合数学
政治
政治学
纯数学
法学
作者
Yuxuan Li,Qibin Hou,Zhaohui Zheng,Ming–Ming Cheng,Jian Yang,Xiang Li
标识
DOI:10.1109/iccv51070.2023.01540
摘要
Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes the lightweight Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing object detection. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard benchmarks, i.e., HRSC2016 (98.46% mAP), DOTA-v1.0 (81.85% mAP), and FAIR1M-v1.0 (47.87% mAP).
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