LHA-YOLO: A Lightweight and High-Accuracy Detector via Parallel Attention and Divide-and-Conquer Fusion for UAV Images

Small-object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to limited pixel representation, complex backgrounds, and insufficient feature discriminability. While one-stage detectors like YOLO offer a favorable speed-accuracy trade-off, their performance on small objects is often hampered by conflicts between semantic and spatial information during multi-scale feature fusion in existing networks. To address this, we propose LHA-YOLO, a lightweight and high-accuracy network based on YOLO11. The network is built upon two core innovations. The first is the Lightweight Feature Extraction Module (LFEM), which employs a parallel spatial-channel attention mechanism to extract discriminative cross-dimensional features efficiently and with low computational cost. The second is the Divide-and-Conquer Propagation Path (DCPP) strategy. This strategy decouples and separately optimizes the handling of semantic and spatial information within its bidirectional propagation paths. To achieve this, the top-down path utilizes the Channel Attention-guided Semantic Aggregation (CASA) module to enhance semantic consistency. In parallel, the bottom-up path employs the Spatial Attention-guided Detail Aggregation (SADA) module to preserve spatial fidelity. Extensive evaluation on the VisDrone and UAVDT datasets shows that LHA-YOLO strikes a favorable balance between performance and efficiency. On VisDrone, it improves mAP50 from 39.4% to 41.6% and mAP50–95 from 23.5% to 24.9% over YOLOv11s. On UAVDT, it raises mAP50 from 32.2% to 36.9% and mAP50–95 from 19.4% to 22.9%, while reducing GFLOPs from 21.3 to 18.8. These results confirm the efficacy of our design for real-time UAV applications.