Remote sensing images pose distinct challenges for downstream tasks due to
their inherent complexity. While a considerable amount of research has been
dedicated to remote sensing classification, object detection and semantic
segmentation, most of these studies have overlooked the valuable prior
knowledge embedded within remote sensing scenarios. Such prior knowledge can be
useful because remote sensing objects may be mistakenly recognized without
referencing a sufficiently long-range context, which can vary for different
objects. This paper considers these priors and proposes a lightweight Large
Selective Kernel Network (LSKNet) backbone. 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 images.
Without bells and whistles, our lightweight LSKNet sets new state-of-the-art
scores on standard remote sensing classification, object detection and semantic
segmentation benchmarks. Our comprehensive analysis further validated the
significance of the identified priors and the effectiveness of LSKNet. The code
is available at this https URL

该研究考虑了远程感知场景中的先验知识，并提出了轻量级大型选择性核网络（LSKNet）作为其骨干网络。LSKNet 能够动态调整其大空间接受野，以更好地建模远程感知场景中各种对象的范围背景。通过在常规远程感知分类、目标检测和语义分割基准上取得了新的最先进成绩，进一步验证了所识别的先验知识的重要性和 LSKNet 的有效性。

LSKNet: 一种面向遥感的基础轻量级骨干网络

LSKNet: A Foundation Lightweight Backbone for Remote Sensing

In the realm of aerial image analysis, object detection plays a pivotal role,
with significant implications for areas such as remote sensing, urban planning,
and disaster management. This study addresses the inherent challenges in this
domain, notably the detection of small objects, managing densely packed
elements, and accounting for diverse orientations. We present an in-depth
evaluation of an object detection model that integrates the Large Selective
Kernel Network (LSKNet)as its backbone with the DiffusionDet head, utilizing
the iSAID dataset for empirical analysis. Our approach encompasses the
introduction of novel methodologies and extensive ablation studies. These
studies critically assess various aspects such as loss functions, box
regression techniques, and classification strategies to refine the model's
precision in object detection. The paper details the experimental application
of the LSKNet backbone in synergy with the DiffusionDet heads, a combination
tailored to meet the specific challenges in aerial image object detection. The
findings of this research indicate a substantial enhancement in the model's
performance, especially in the accuracy-time tradeoff. The proposed model
achieves a mean average precision (MAP) of approximately 45.7%, which is a
significant improvement, outperforming the RCNN model by 4.7% on the same
dataset. This advancement underscores the effectiveness of the proposed
modifications and sets a new benchmark in aerial image analysis, paving the way
for more accurate and efficient object detection methodologies. The code is
publicly available at this https URL

本研究采用 LSKNet 骨干网络结构和 DiffusionDet 头部模型，通过新颖方法和大量割除研究对对象检测模型的精确性进行了深入评估，在空中图像对象检测中取得显著性能提升，提出了新的基准，为更精确和高效的对象检测方法铺平了道路。