Small object is one of the primary challenges in the field of object detection, which is notably pronounced to the detection in the images from Unmanned Aerial Vehicles (UAV). Existing detectors based on deep-learning methods usually apply the feature extraction networks with a large down-sampling factor to obtain higher-level features. However, such big stride tends to make the feature information of small objects become the little point or even vanish in the low-resolution feature maps due to the limitation of pixels. Therefore, a novel structure called Multi-branch Parallel Feature Pyramid Networks (MPFPN) is proposed in this article, which aims to extract more abundant feature information of the objects with a small size. Specifically, the parallel branch is designed to recover the features that missed in the deeper layers. Meanwhile, a supervised spatial attention module (SSAM) is applied to weaken the impact of background noise inference and focus object information. Furthermore, we adopt cascade architecture in the Fast R-CNN stage for a more powerful localization capability. Experiments on the public drone-based datasets named VisDrone-DET demonstrate that our method achieves competitive performance compared with other state-of-the-art detection algorithms.

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.

Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved by integrating the degree information and label information of nodes. Then, the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model. At last, the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification, network reconstruction and link prediction tasks, compared with three benchmark algorithms.