Lightweight Detection Network for Arbitrary-Oriented Vehicles in UAV Imagery via Global Attentive Relation and Multi-Path Fusion

Recent advances in unmanned aerial vehicles (UAVs) have increased altitude capability in road-traffic monitoring. However, state-of-the-art vehicle detection methods still lack accurate abilities and lightweight structures in the UAV platform due to the background uncertainties, scales, densities, shapes, and directions of objects resulting from the UAV imagery's shooting angle. We propose a lightweight solution to detect arbitrary-oriented vehicles under uncertain backgrounds, varied resolutions, and illumination conditions. We first present a cross-stage partial bottleneck transformer (CSP BoT) module to exploit the global spatial relationship captured by multi-head self-attention, validating its implication in recessive dependencies. We then propose an angle classification prediction branch in the YOLO head network to detect arbitrarily oriented vehicles in UAV images and employ a circular smooth label (CSL) to reduce the classification loss. We further improve the multi-scale feature maps by combining the prediction head network with the adaptive spatial feature fusion block (ASFF-Head), which adapts the spatial variation of prediction uncertainties. Our method features a compact, lightweight design that automatically recognizes key geometric factors in the UAV images. It demonstrates superior performance under environmental changes while it is also easy to train and highly generalizable. This remarkable learning ability makes the proposed method applicable to geometric structure and uncertainty estimates. Extensive experiments on the UAV vehicle dataset UAV-ROD and remote sensing dataset UACS-AOD demonstrate the superiority and cost-effectiveness of the proposed method, making it practical for urban traffic and public security.