CBW-DETR: A Lightweight Detection Transformer for Small Object Detection in UAV Imagery

Small object detection in Unmanned Aerial Vehicle (UAV) imagery faces critical challenges, including extreme scale variations, dense spatial distributions, and stringent computational constraints, for real-time deployment. To address these challenges, this paper proposes a CBW-based Detection Transformer (CBW-DETR), an enhanced transformer-based detection framework that integrates architectural efficiency with scale-aware mechanisms throughout the detection pipeline. The framework comprises three coordinated innovations. First, a Context-Guided Feature Extraction (ContextGFE) module reduces model parameters and theoretical computational cost through adaptive receptive field selection and wavelet-domain enhancement while maintaining representational capacity. Second, a Scale-Aware Feature Pyramid Network (SAFPN) employs spatial-variant compensation factors and cross-scale attention to facilitate balanced gradient flow across pyramid levels, particularly benefiting small object detection. Third, an Adaptive Scale IoU (ASIoU) loss function implements uncertainty-aware gradient modulation and scale-specific optimization to enhance localization accuracy for objects of varying sizes. Extensive experiments on VisDrone2019 and Dataset for Object Detection in Aerial Images (DOTA) datasets demonstrate that CBW-DETR achieves substantial improvements in detection accuracy while reducing model parameters by 28.1% and theoretical computation by 18.0% compared to the Real-Time Detection Transformer-R18 (RT-DETR-R18) baseline. These reductions in model complexity come at a moderate cost in inference throughput (73.6 frames per second (FPS) vs. 94.1 FPS), attributable to memory-access-intensive operations introduced by multi-branch convolutions and wavelet transforms. Among the evaluated detectors, including You Only Look Once (YOLO) series variants and transformer-based methods, CBW-DETR achieves a competitive detection accuracy with a notably compact model footprint. Visualization analysis confirms its robust performance across diverse challenging scenarios including nighttime conditions, dense object distributions, and severe occlusions, validating the framework’s practical applicability for UAV-based detection applications.