An Efficient Method for Detecting Dense and Small Objects in UAV Images

Abstract

Object detection in unmanned aerial vehicle (UAV) images is an important and challenging task for many applications, which often needs highly efficient detection algorithms to meet the accuracy and real-time requirements of the applications. In this article, we investigate efficient mechanisms for detecting dense and small objects in UAV images. Specifically, 1) kernel K-means is used to obtain optimal anchors for dense and small object detection; 2) a spatial information enhancement module is proposed to improve the detection accuracy of dense objects by extracting object spatial location information; 3) a Coord_C3 module is proposed to improve the receptive field of the network and to reduce the number of network parameters; and 4) a small detection head is added in the Head of the network and skip connections are employed in the Neck of the network to improve the detection accuracy of small objects. Experimental results on the VisDrone-2019, LEVIR-ship, and Stanford Drone datasets show that our method not only has higher detection accuracy but also runs faster compared to state-of-the-art detection methods.

Keywords

• Deep learning
• kernel K-means
• object detection
• spatial information
• unmanned aerial vehicle (UAVs)