UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery
It addresses the need for more effective object detection in UAV imagery, which is used in various scenarios, but is incremental as it adapts existing DETR methods to a specific domain.
This paper tackles the problem of inefficient object detection in unmanned aerial vehicle (UAV) imagery by proposing UAV-DETR, an end-to-end detection transformer framework tailored for UAV data, which improves AP by 3.1% and AP50 by 4.2% over the baseline on the VisDrone dataset.
Unmanned aerial vehicle object detection (UAV-OD) has been widely used in various scenarios. However, most existing UAV-OD algorithms rely on manually designed components, which require extensive tuning. End-to-end models that do not depend on such manually designed components are mainly designed for natural images, which are less effective for UAV imagery. To address such challenges, this paper proposes an efficient detection transformer (DETR) framework tailored for UAV imagery, i.e., UAV-DETR. The framework includes a multi-scale feature fusion with frequency enhancement module, which captures both spatial and frequency information at different scales. In addition, a frequency-focused down-sampling module is presented to retain critical spatial details during down-sampling. A semantic alignment and calibration module is developed to align and fuse features from different fusion paths. Experimental results demonstrate the effectiveness and generalization of our approach across various UAV imagery datasets. On the VisDrone dataset, our method improves AP by 3.1\% and $\text{AP}_{50}$ by 4.2\% over the baseline. Similar enhancements are observed on the UAVVaste dataset. The project page: https://github.com/ValiantDiligent/UAV-DETR