UAV Video Vehicle Object Detection Based on Spatio-Temporal Feature Enhancement

In recent years, vehicle detection in unmanned aerial vehicle (UAV) videos has attracted significant attention. However, the traditional detection paradigm based on single frame image is faced with the performance bottleneck caused by rare pose, motion blur and occlusion. To address these issues, we propose a novel network comprising three important modules designed to fully exploit the spatiotemporal contextual information across video frames. Firstly, To strengthen the backbone network's capacity for extracting global contextual information from single-frame images, we propose the Visual State Space Context Module (VSSCM). By incorporating the 2D-Selective-Scan module (SS2D), VSSCM captures global dependencies and enriches contextual information without significantly increasing computational complexity. Secondly, the Temporal Information-Guided Spatial Attention Aggregation Module (TGSAM) is introduced to fuse features from critical regions in adjacent frames. Finally, the Self-Attention-based Classification Feature Aggregation Module (SACFAM) is designed to model the relation between object features across frames and perform feature aggregation based on the learned relation matrix, thereby effectively improving the quality of classification features in UAV videos. Extensive experiments are conducted on the challenging VisDrone2019-VID dataset, and the experimental results demonstrate the effectiveness and superiority of the proposed method.