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

We propose DiffusionDet, a new framework that formulates object detection as
a denoising diffusion process from noisy boxes to object boxes. During training
stage, object boxes diffuse from ground-truth boxes to random distribution, and
the model learns to reverse this noising process. In inference, the model
refines a set of randomly generated boxes to the output results in a
progressive way. The extensive evaluations on the standard benchmarks,
including MS-COCO and LVIS, show that DiffusionDet achieves favorable
performance compared to previous well-established detectors. Our work brings
two important findings in object detection. First, random boxes, although
drastically different from pre-defined anchors or learned queries, are also
effective object candidates. Second, object detection, one of the
representative perception tasks, can be solved by a generative way. Our code is
available at this https URL

DiffusionDet 是一种将对象检测作为从噪声框到对象框的去噪扩散过程的新框架，其在训练阶段通过对象框从地面实况框扩散到随机分布，模型学习将该过程反转，在推断阶段，模型以渐进方式将一组随机生成框细化为输出结果，使用随机框作为对象候选框有利于解决对象检测问题，并且该问题可以通过生成方法来解决。