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 1 6 0 × 1 6 0 microdetection (P2) replacing 20 × 20 head (P5) and SCAM suppressing background interference via dual attention. Evaluations on VisDrone2019 show BOAD-YOLO achieves 4 5. 6 % mAP@0.5 ($+7.6 %$ vs YOLOv8s) and 28.2 % mAP @ 0.5: 0.95 ($+5.1 %$) with only 8.3 M parameters and 36.0 GFLOPs, providing an efficient solution for resource-constrained scenarios.