Small Aircraft Detection Based on Feature Enhancement and Context Information

Abstract

In the field of civil aviation safety and air security, research on identification and tracking of small aircraft is very important. Aiming at the problem of low recognition rate of long-distance small air targets, this study proposes a method of GST-YOLOv3 combined with DeepSORT algorithm to detect and track small aircraft. The proposed GST-YOLOv3 uses a ghost module to extract effective features and simplify the network. In feature extraction layer, the spatial pyramid pooling layer is added to fuse local features and global features to enhance the features of small targets. The Transformer module is embedded in the feature fusion layer to focus on global context information. In the multiscale detection layer, a new large-scale feature map detection layer is formed by fusing shallow features and deep features to obtain a smaller receptive field. To avoid frame loss of video detection, the DeepSORT algorithm is used for real-time tracking. In this study, the self-made data set Aplanes and the public data set VisDroneDET are used to verify the effect of the proposed GST-YOLOv3. The computation of the GST-YOLOv3 was reduced by 45.8% compared with YOLOv3; the missed detection rate of airplanes decreased by 2.5%; and the detection precision, recall rate, F1 value, and average precision/0.5 value of small aircraft reached 98.4, 98.7, 98.5, and 98.6%, respectively, which are better than those of similar literature.