Abstract
Object detection in Unmanned Aerial Vehicle (UAV) images plays fundamental roles in a wide variety of applications. As UAVs are maneuverable with high speed, multiple viewpoints, and varying altitudes, objects in UAV images are distributed with great heterogeneity, varying in size, with high density, bringing great difficulty to object detection using existing algorithms. To address the above issues, we propose a novel global density fused convolutional network (GDF-Net) optimized for object detection in UAV images. We test the effectiveness and robustness of the proposed GDF-Nets on the VisDrone dataset and the UAVDT dataset. The designed GDF-Net consists of a Backbone Network, a Global Density Model (GDM), and an Object Detection Network. Specifically, GDM refines density features via the application of dilated convolutional networks, aiming to deliver larger reception fields and to generate global density fused features. Compared with base networks, the addition of GDM improves the model performance in both recall and precision. We also find that the designed GDM facilitates the detection of objects in congested scenes with high distribution density. The presented GDF-Net framework can be instantiated to not only the base networks selected in this study but also other popular object detection models.
Highlights
Unmanned Aerial Vehicle (UAV) is a new and prominent remote sensing platform operated by radio remote control equipment or programming, which benefits a wide range of practical applications that include environmental monitoring [1,2,3,4,5], abnormal target tracking [6,7] and animal protection [8,9,10]
Object detection in UAV imagery remains a challenging task, as UAVs are often maneuverable with high speed, multiple viewpoints, and varying altitudes, which leads to unique characteristics of UAV imagery that usually contain varying perspectives, scales, and occlusion
Objects in UAV images are often distributed with heterogeneity, varying in size, with high density, causing great difficulty for object detection using existing algorithms that are not optimized for UAV images
Summary
Unmanned Aerial Vehicle (UAV) is a new and prominent remote sensing platform operated by radio remote control equipment or programming, which benefits a wide range of practical applications that include environmental monitoring [1,2,3,4,5], abnormal target tracking [6,7] and animal protection [8,9,10]. The rapid development in UAV techniques and applications has fostered wide attention in the object detection domain. We focus on object detection in UAV images [11,12] that aims to identify and localize objects of interest from UAV images, serving as a basic and significant algorithm in numerous UAV applications. In order to detect objects in UAV images, early algorithms adopt background extraction and selected feature extraction approaches [6,13,14,15]. Deep convolutional neural networks (DCNNs), an important network model in deep learning, brings significant progress and achieves state-of-the-art performance in image analysis related fields. Unlike generic natural scenes with large individual objects, UAV images often contain a large number of small objects, leading to great challenges for object detection in UAV images using existing approaches [28,29]
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