EFD-YOLO: An Improved YOLOv8 Network for River Floating Debris Object Detection

With the rapid development of unmanned aerial vehicle (UAV) technology, UAVs have provided an innovative solution for floating debris monitoring. However, object detection in UAV images remains challenging due to high miss rates for small objects, insufficient low-level feature extraction and computational redundancy. This letter proposes an Efficient Floating Debris detection model based on YOLOv8n, named EFD-YOLO, to address these issues. First, the Edge Fusion Stem (EFStem) module is proposed to enhance low-level feature extraction through an integrated gate-attention mechanism. Second, the Multi-Branch Efficient Reparameterization Block (MBERB) is designed to achieve efficient cross-layer feature fusion. Experimental results demonstrate that compared to YOLOv8n, our model achieves a 6.3% improvement in mean Average Precision (mAP) on the UAV Floating Debris Dataset, while simultaneously reducing parameters by 26.7% and improving small object recall by 21.9%. The inference time of EFD-YOLO on the RK3588 edge device is as low as 30.5 ms, demonstrating real-time capability.