MFEF-YOLO: A Multi-Scale Feature Extraction and Fusion Network for Small Object Detection in Aerial Imagery over Open Water

Current object detection using UAV platforms in open water faces challenges such as low detection accuracy, limited storage, and constrained computational capabilities. To address these issues, we propose MFEF-YOLO, a small object detection network based on multi-scale feature extraction and fusion. First, we introduce a Dual-Branch Spatial Pyramid Pooling Fast (DBSPPF) module in the backbone network to replace the original SPPF module, while integrating ODConv and C3k2 modules to collectively enhance feature extraction capabilities. Second, we improve small object detection by adding a P2 detection head and reduce model parameters by removing the P5 detection head. Finally, we design an Island-based Multi-scale Feature Fusion Network (IMFFNet) and employ a Coordinate-guided Multi-scale Feature Fusion Module (CMFFM) to strengthen contextual information and boost detection accuracy. We validate the effectiveness of MFEF-YOLO using the public dataset SeaDronesSee and our custom dataset TPDNV. Experimental results show that compared to the baseline model, mAP50 improves by 0.11 and 0.03 using the two datasets, respectively, while model parameters are reduced by 11.54%. Furthermore, DBSPPF and IMFFNet demonstrate superior performance in comparative studies with other methods, confirming their effectiveness. These improvements and outstanding performance make MFEF-YOLO particularly suitable for UAV-based object detection in open waters.