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, and the long-range context required by different types of objects can vary. In this paper, we take these priors into account and propose the 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 the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection. Without bells and whistles, LSKNet sets new state-of-the-art scores on standard benchmarks, i.e., HRSC2016 (98.46\% mAP), DOTA-v1.0 (81.64\% mAP) and FAIR1M-v1.0 (47.87\% mAP). Based on a similar technique, we rank 2nd place in 2022 the Greater Bay Area International Algorithm Competition. Code is available at https://github.com/zcablii/Large-Selective-Kernel-Network.

中文翻译：

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用于遥感目标检测的大型选择性核网络

最近对遥感目标检测的研究主要集中在改进定向边界框的表示，但忽视了遥感场景中呈现的独特先验知识。这种先验知识可能很有用，因为在没有参考足够长距离的上下文的情况下，可能会错误地检测到微小的遥感对象，并且不同类型的对象所需的远程上下文可能会有所不同。在本文中，我们考虑了这些先验知识并提出了大型选择性核网络 (LSKNet)。LSKNet 可以动态调整其较大的空间感受野，以更好地模拟遥感场景中各种物体的测距环境。据我们所知，这是首次在遥感目标检测领域探索大型选择性核机制。没有花里胡哨的东西，LSKNet 在标准基准上设置了新的最先进的分数，即 HRSC2016 (98.46\% mAP)、DOTA-v1.0 (81.64\% mAP) 和 FAIR1M-v1.0 (47.87\% mAP) ％ 地图）。基于类似的技术，我们在2022年大湾区国际算法大赛中获得第二名。代码可在 https://github.com/zcablii/Large-Selective-Kernel-Network 获得。