A Lightweight Small-Object Detector for UAV Imagery via Multi-Scale Feature Enhancement and Saliency-Guided Cross-Layer Fusion

Abstract

As unmanned aerial vehicles (UAVs) become central to traffic inspection, urban security, and emergency response, UAV-based environmental perception requires both high accuracy and real-time efficiency. However, UAV imagery remains challenging due to three primary factors: detail loss, where small targets occupy minimal pixels and weak edges are diluted by downsampling; ineffective cross-scale fusion, where semantic gaps between shallow and deep features lead to scale misalignment and small-object suppression; and environmental interference, where clutter, occlusion, and dense layouts cause localization drift. To address these challenges, we propose an optimized efficient detector built upon the YOLOv8s framework, incorporating multi-scale feature enhancement and saliency-guided cross-layer fusion. Specifically, we integrate RFCAConv and RGCSP modules into the backbone to strengthen local detail and spatial structure modeling. Furthermore, we design a Multi-Scale Adaptive Fusion Module (MSAFM) to align deep and shallow cues through dual-pooling and adaptive channel recalibration. To handle complex backgrounds, a Saliency-Guided Contextual Attention Module (CASM) is introduced to emphasize target regions, alongside a dynamic detection head for adaptive feature modulation. Evaluated on the VisDrone2019 dataset, our method achieves 48.3% mAP@0.5 and 29.0% mAP@[0.5:0.95], outperforming YOLOv8s by 10.2 and 6.3 points, respectively, while keeping the model compact with 7.2M parameters and a 14.4 MB model size.