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 cluster-based 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.
Code will be available in https://github.com/fyangneil.

本论文提出了一种利用聚类检测（ClusDet）网络来解决机载图像中小物体检测困难的问题，并在三个通用数据集上展现了其与其他检测器相比很有潜力的表现。