Clustered Object Detection in Aerial Images

Fan Yang, Heng Fan, Peng Chu, Erik Blasch, Haibin Ling

Abstract:

Detecting objects in aerial images is challenging for at
least two reasons: (1) target objects like pedestrians are
very small in pixels, making them hardly distinguished from
surrounding background; and (2) targets are in general
sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues
inspired by observing that these targets are often clustered.
In particular, we propose a Clustered Detection (ClusDet)
network that unifies object clustering and detection in an
end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection
network (DetecNet). Given an input image, CPNet produces
object cluster regions and ScaleNet estimates object scales
for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has
several advantages over previous solutions: (1) it greatly
reduces the number of chips for final object detection and
hence achieves high running time efficiency, (2) the clusterbased scale estimation is more accurate than previously
used single-object based ones, hence effectively improves
the detection for small objects, and (3) the final DetecNet
is dedicated for clustered regions and implicitly models the
prior context information so as to boost detection accuracy.
The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all
experiments, ClusDet achieves promising performance in
comparison with state-of-the-art detectors.