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。

航空影像领域的创新视野：LSKNet 与 DiffusionDet 在高级目标检测方面的应用

Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for  Advanced Object Detection

Aerial image analysis at a semantic level is important in many applications
with strong potential impact in industry and consumer use, such as automated
mapping, urban planning, real estate and environment monitoring, or disaster
relief. The problem is enjoying a great interest in computer vision and remote
sensing, due to increased computer power and improvement in automated image
understanding algorithms. In this paper we address the task of automatic
geolocalization of aerial images from recognition and matching of roads and
intersections. Our proposed method is a novel contribution in the literature
that could enable many applications of aerial image analysis when GPS data is
not available. We offer a complete pipeline for geolocalization, from the
detection of roads and intersections, to the identification of the enclosing
geographic region by matching detected intersections to previously learned
manually labeled ones, followed by accurate geometric alignment between the
detected roads and the manually labeled maps. We test on a novel dataset with
aerial images of two European cities and use the publicly available
OpenStreetMap project for collecting ground truth roads annotations. We show in
extensive experiments that our approach produces highly accurate localizations
in the challenging case when we train on images from one city and test on the
other and the quality of the aerial images is relatively poor. We also show
that the the alignment between detected roads and pre-stored manual annotations
can be effectively used for improving the quality of the road detection
results.

本文提出一种新颖的方法，通过识别和匹配道路和交叉口，实现对空中图像进行自动地理定位，并且使用 OpenStreetMap 收集地面真实道路注释，在数个实验中体现了高精度的本地化效果，同时还表明检测到的道路和以前手动标记的地图之间的几何对准可以有效地用于改进道路检测结果。