Unmanned aerial vehicle （UAV） object detection plays a crucial role in maritime rescue missions. However， the varying perspectives and altitudes inherent in UAV aerial photography lead to multi-scale variations in object individuals and vessels. Additionally， the glare resulting from sunlight reflecting off the sea surface can cause false detection issues. To address these challenges and meet the lightweight requirements of real-time object detection algorithms for UAVs， this paper proposes a lightweight UAV maritime rescue object detection algorithm based on dynamic progressive fusion （DPF-YOLO）， using YOLOv8n as the baseline network. Firstly， we introduce a lightweight redundant information extraction module （RIEM） that reduces redundant information in feature maps， highlighting key features to mitigate false detections caused by glare. Secondly， we propose a dynamic multi-scale feature extraction module （DMFEM） that dynamically adjusts the receptive field to accommodate objects of varying scales， enhancing multi-scale feature representation capabilities. Finally， by integrating the DMFEM module， we develop a dynamic progressive fusion network （DPFNet）. This network employs a progressive fusion structure to reduce semantic differences between non-adjacent layers with objects of different scales， thereby improving multi-scale feature fusion. DPF-YOLO is designed with P2， P3 and P4 detection layer structure to accommodate the object sizes in maritime rescue scenarios， enrich multi-scale information， and enhance feature extraction for small objects. Experimental results on the SeaDronesSee v2 dataset demonstrate that DPF-YOLO achieves a detection accuracy of mAP_0.5 = 72.2% with only 1.19 M of parameters. Compared to the baseline network YOLOv8n， DPF-YOLO reduces the number of parameters by 60.5%， increases the recall rate by 12.4%， and improves precision by 8.2%. The generalization experimental results on the VisDrone dataset demonstrate that DPF-YOLO possesses excellent generalization capabilities.