BOAD-YOLO: Optimizing YOLOv8 with Attention and Feature Fusion for Small Object Detection

To address feature degradation, scale sensitivity, and background interference in small object detection, we propose BOAD-YOLO-an enhanced YOLOv8s framework. Key innovations include: Backbone-integrated coordinate attention establishing long-range spatial dependencies; Neck with BiFPN for multi-scale feature fusion, DySample replacing standard downsampling to reduce computation, and nested coordinate attention enabling dual optimization; Head with $\mathbf{1 6 0} \times \mathbf{1 6 0}$ microdetection (P2) replacing $20 \times 20$ head (P5) and SCAM suppressing background interference via dual attention. Evaluations on VisDrone2019 show BOAD-YOLO achieves $\mathbf{4 5. 6 \%}$ [email protected] ($+7.6 \%$ vs YOLOv8s) and $28.2 \% \mathrm{mAP} \text{@} 0.5: 0.95$ ($+5.1 \%$) with only 8.3 M parameters and 36.0 GFLOPs, providing an efficient solution for resource-constrained scenarios.