YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
Chenguang Liu Guangshuai Gao Ziyue Huang Zhenghui Hu Qingjie Liu Yunhong Wang
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.