2 research outputs found
Guided Attention Network for Object Detection and Counting on Drones
Object detection and counting are related but challenging problems,
especially for drone based scenes with small objects and cluttered background.
In this paper, we propose a new Guided Attention Network (GANet) to deal with
both object detection and counting tasks based on the feature pyramid.
Different from the previous methods relying on unsupervised attention modules,
we fuse different scales of feature maps by using the proposed
weakly-supervised Background Attention (BA) between the background and objects
for more semantic feature representation. Then, the Foreground Attention (FA)
module is developed to consider both global and local appearance of the object
to facilitate accurate localization. Moreover, the new data argumentation
strategy is designed to train a robust model in various complex scenes.
Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and
PUCPR+) show the state-of-the-art detection and counting performance of the
proposed method compared with existing methods
Perceiving Traffic from Aerial Images
Drones or UAVs, equipped with different sensors, have been deployed in many
places especially for urban traffic monitoring or last-mile delivery. It
provides the ability to control the different aspects of traffic given
real-time obeservations, an important pillar for the future of transportation
and smart cities. With the increasing use of such machines, many previous
state-of-the-art object detectors, who have achieved high performance on front
facing cameras, are being used on UAV datasets. When applied to high-resolution
aerial images captured from such datasets, they fail to generalize to the wide
range of objects' scales. In order to address this limitation, we propose an
object detection method called Butterfly Detector that is tailored to detect
objects in aerial images. We extend the concept of fields and introduce
butterfly fields, a type of composite field that describes the spatial
information of output features as well as the scale of the detected object. To
overcome occlusion and viewing angle variations that can hinder the
localization process, we employ a voting mechanism between related butterfly
vectors pointing to the object center. We evaluate our Butterfly Detector on
two publicly available UAV datasets (UAVDT and VisDrone2019) and show that it
outperforms previous state-of-the-art methods while remaining real-time