This paper proposes a novel approach to object detection on drone imagery,
namely Multi-Proxy Detection Network with Unified Foreground Packing
(UFPMP-Det). To deal with the numerous instances of very small scales,
different from the common solution that divides the high-resolution input image
into quite a number of chips with low foreground ratios to perform detection on
them each, the Unified Foreground Packing (UFP) module is designed, where the
sub-regions given by a coarse detector are initially merged through clustering
to suppress background and the resulting ones are subsequently packed into a
mosaic for a single inference, thus significantly reducing overall time cost.
Furthermore, to address the more serious confusion between inter-class
similarities and intra-class variations of instances, which deteriorates
detection performance but is rarely discussed, the Multi-Proxy Detection
Network (MP-Det) is presented to model object distributions in a fine-grained
manner by employing multiple proxy learning, and the proxies are enforced to be
diverse by minimizing a Bag-of-Instance-Words (BoIW) guided optimal transport
loss. By such means, UFPMP-Det largely promotes both the detection accuracy and
efficiency. Extensive experiments are carried out on the widely used VisDrone
and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much
higher speed, highlighting its advantages.

该论文提出了一种新的无人机图像目标检测方法，即具有统一前景包装的多代理检测网络（UFPMP-Det），通过多代理学习和合并来提高检测精度和速度。

针对无人机图像的准确高效目标检测：UFPMP-Det

UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone  Imagery

Drones, or general UAVs, equipped with cameras have been fast deployed with a
wide range of applications, including agriculture, aerial photography, and
surveillance. Consequently, automatic understanding of visual data collected
from drones becomes highly demanding, bringing computer vision and drones more
and more closely. To promote and track the developments of object detection and
tracking algorithms, we have organized three challenge workshops in conjunction
with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around
the world. We provide a large-scale drone captured dataset, VisDrone, which
includes four tracks, i.e., (1) image object detection, (2) video object
detection, (3) single object tracking, and (4) multi-object tracking. In this
paper, we first present a thorough review of object detection and tracking
datasets and benchmarks, and discuss the challenges of collecting large-scale
drone-based object detection and tracking datasets with fully manual
annotations. After that, we describe our VisDrone dataset, which is captured
over various urban/suburban areas of 14 different cities across China from
North to South. Being the largest such dataset ever published, VisDrone enables
extensive evaluation and investigation of visual analysis algorithms for the
drone platform. We provide a detailed analysis of the current state of the
field of large-scale object detection and tracking on drones, and conclude the
challenge as well as propose future directions. We expect the benchmark largely
boost the research and development in video analysis on drone platforms. All
the datasets and experimental results can be downloaded from
this https URL

本文介绍了一个被称作 VisDrone 的大规模数据集并探讨了无人机平台上的目标检测与跟踪算法。