论文标题
密集的多尺度特征融合金字塔网络用于无人机捕获图像中的对象检测
Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images
论文作者
论文摘要
尽管用深度学习的对象检测研究领域已经取得了很大的进展，但对于尺寸较小的对象仍然存在着一项具有挑战性的任务，这在无人机捕获的图像中显着。在解决这些问题时，探索功能提取方法的迫切需要，该方法可以提取小物体的更多特征信息。在本文中，我们提出了一种称为密集的多尺度特征融合金字塔网络（DMFFPN）的新颖方法，该方法旨在尽可能获得丰富的特征，从而改善信息的传播和重复使用。具体而言，密集的连接旨在完全利用不同卷积层的表示形式。此外，在第二阶段应用级联体系结构以增强本地化能力。名为Visdrone-Det的基于无人机数据集的实验表明我们的方法具有竞争性能。
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.
