Detecting objects from aerial images poses significant challenges due to the following factors: 1) Aerial images typically have very large sizes, generally with millions or even hundreds of millions of pixels, while computational resources are limited. 2) Small object size leads to insufficient information for effective detection. 3) Non-uniform object distribution leads to computational resource wastage. To address these issues, we propose YOLC (You Only Look Clusters), an efficient and effective framework that builds on an anchor-free object detector, CenterNet. To overcome the challenges posed by large-scale images and non-uniform object distribution, we introduce a Local Scale Module (LSM) that adaptively searches cluster regions for zooming in for accurate detection. Additionally, we modify the regression loss using Gaussian Wasserstein distance (GWD) to obtain high-quality bounding boxes. Deformable convolution and refinement methods are employed in the detection head to enhance the detection of small objects. We perform extensive experiments on two aerial image datasets, including Visdrone2019 and UAVDT, to demonstrate the effectiveness and superiority of our proposed approach.

翻译：从航拍图像中检测目标面临以下重大挑战：1）航拍图像通常尺寸极大，一般包含数百万甚至上亿像素，而计算资源有限；2）目标尺寸过小导致有效检测信息不足；3）目标分布不均匀造成计算资源浪费。为解决上述问题，本文提出YOLC（You Only Look Clusters）框架——一种基于无锚点检测器CenterNet的高效检测框架。针对大尺度图像与非均匀目标分布带来的挑战，我们引入局部尺度模块（LSM）自适应搜索聚类区域并放大检测。同时，采用高斯瓦瑟斯坦距离（GWD）改进回归损失以获取高质量边界框，并在检测头中引入可变形卷积与精化方法增强小目标检测能力。在Visdrone2019和UAVDT两个航拍图像数据集上的广泛实验证明了所提方法的有效性与优越性。