  Although much significant progress has been made in the research field of
object detection with deep learning, there still exists a challenging task for
the objects with small size, which is notably pronounced in UAV-captured
images. Addressing these issues, it is a critical need to explore the feature
extraction methods that can extract more sufficient feature information of
small objects. In this paper, we propose a novel method called Dense Multiscale
Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich
features as much as possible, improving the information propagation and reuse.
Specifically, the dense connection is designed to fully utilize the
representation from the different convolutional layers. Furthermore, cascade
architecture is applied in the second stage to enhance the localization
capability. Experiments on the drone-based datasets named VisDrone-DET suggest
a competitive performance of our method.
