  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.
