中圖分類號：TP391.41 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.257615 中文引用格式： 李一鵬，楊華. 面向無人機的深度學習內河船舶小目標檢測方法[J]. 電子技術應用，2026，52(4)：1-9. 英文引用格式： Li Yipeng，Yang Hua. A deep learning-based method for small target detection of inland river vessels oriented to UAVs[J]. Application of Electronic Technique，2026，52(4)：1-9.
A deep learning-based method for small target detection of inland river vessels oriented to UAVs
Li Yipeng，Yang Hua
School of Information Engineering， Shanghai Maritime University
Abstract： In vessel detection from low-altitude UAV perspectives in inland rivers, traditional algorithms struggle to accurately detect small vessels due to issues such as small target size, vessel occlusion, complex backgrounds, light reflection, and wave disturbances. To address these problems, this study proposes an improved algorithm based on YOLOv11n—YOLO11-FFW (YOLO11—FEM FFM_Concat WIoUv2). To enhance the feature extraction ability for small vessel targets, the Feature Enhancement Module (FEM) is introduced, which expands the receptive field through multi-branch atrous convolution and integrates multi-scale contextual information. To improve multi-scale feature expression in complex backgrounds, the Feature Fusion Module Concat (FFM_Concat) is introduced, incorporating a learnable weight recalibration mechanism on top of the BiFPN structure, achieving adaptive fusion of high- and low-level features. To increase the model's robustness in scenarios with water surface reflection, occlusion, and dense targets, the loss function is improved to WIoUv2, dynamically balancing localization and classification losses. Experimental results show that compared to YOLOv11, YOLO11-FFW achieves a 1.4% increase in mAP@0.5, a 0.8% increase in precision, and a 2.4% increase in recall, which is verified to be effective in detecting small vessels in inland river scenarios from complex UAV perspectives.
Key words : deep learning；YOLOv11；inland river vessel detection from UAV perspectives；small object detection；multi-scale feature fusion；WIoUv2
