The UAV Benchmark: Compact Detection of Vehicles in Urban Scenarios

Vehicle detection in unmanned aerial vehicle (UAV) images is a fundamental task in photogrammetry and remote sensing. While great success has been achieved, this task remains challenging due to two aspects: 1) the limitation of existing annotation methods in compactly enclosing targets with large perspective distortions in oblique UAV images; 2) the lack of vehicle detection datasets under oblique perspectives. To this end, we propose an oblique UAV benchmark for the precise expression and localization of distorted vehicles in urban scenarios. The benchmark consists of 1) a new parallelogramlike bounding box (PBB) annotation for compactly representing vehicles in oblique UAV images; and 2) a large-scale UAV dataset (namely PARA) for vehicle detection with PBB representation. Our PBB representation frees the angle flexibility to allow a compact depiction of vehicles under various perspective distortion, thus overcoming the inherent limits of rectangular representation [like horizontal bounding box (HBB)] used in traditional annotation methods. PARA comprises 1025 high-resolution images and 117 122 manually annotated object bounding boxes obtained from different UAV platforms. The annotated images are collected from scenarios with complex urban backgrounds and different shooting angles to reflect real-world conditions. Moreover, we compared detection algorithms based on the mainstream HBB and PBB representations on the PARA dataset and established a baseline for UAV oblique image-based vehicle detection. Experimental results validate the effectiveness of PBB representation and highlight the challenges posed by PARA.