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

Yingjie Liu

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

Computer Vision and Pattern Recognition 2020-12-22 v1 Machine Learning Image and Video Processing

Authors: Yingjie Liu

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.

Keywords

object detection uav images small objects feature fusion pyramid networks dense connection visdrone-det

Cite

@article{arxiv.2012.10643,
  title  = {Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images},
  author = {Yingjie Liu},
  journal= {arXiv preprint arXiv:2012.10643},
  year   = {2020}
}

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10 pages, 5 figures

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

Abstract

Keywords

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