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

TL;DR

This paper introduces DMFFPN, a novel dense multiscale feature fusion network designed to improve small object detection in UAV images by enhancing feature extraction and information propagation.

Contribution

It proposes a dense connection and cascade architecture to better extract and utilize features for small object detection in UAV imagery.

Findings

Achieves competitive results on VisDrone-DET dataset

Enhances feature propagation and reuse for small object detection

Improves localization accuracy in UAV images

Abstract

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.