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Although much significant progress has been made in the research field ofobject detection with deep learning, there still exists a challenging task forthe objects with small size, which is notably pronounced in UAV-capturedimages. Addressing these issues, it is a critical need to explore the featureextraction methods that can extract more sufficient feature information ofsmall objects. In this paper, we propose a novel method called Dense MultiscaleFeature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining richfeatures as much as possible, improving the information propagation and reuse.Specifically, the dense connection is designed to fully utilize therepresentation from the different convolutional layers. Furthermore, cascadearchitecture is applied in the second stage to enhance the localizationcapability. Experiments on the drone-based datasets named VisDrone-DET suggesta competitive performance of our method.

Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images