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.

通过建立在CenterNet上的高效和有效的框架YOLC，我们引入了局部尺度模块（LSM）来解决处理大规模图像和非均匀目标分布时的挑战，通过使用高斯Wasserstein距离（GWD）修改回归损失以获得高质量边界框，并在检测头部使用可变形卷积和改进方法来增强对小目标的检测。我们在Visdrone2019和UAVDT等两个航拍图像数据集上进行了大量实验，证明了我们提出的方法的有效性和优越性。

YOLC: 仅聚焦天空图像中微小物体检测的方法