SDP-YOLOv8: A Lightweight Enhancement Algorithm for Small Object Detection in UAV Aerial Photography

To overcome the limitations of existing UAV object detection algorithms—particularly missed detections, false alarms, and the progressive loss of fine-grained features for small objects—this paper proposes SDP-YOLOv8, a lightweight and parameter-efficient enhancement of YOLOv8. The design aims to improve small-object detection accuracy while maintaining a lightweight architecture suitable for deployment on memory-constrained UAV platforms. Four lightweight-oriented modifications are introduced: (1) SCFS, which combines SPD-Conv for low-information-loss downsampling with a C2f block and SimAM attention; (2) DCSPPF, expanding the receptive field via parallel dilated convolutions; (3) a GhostConv-infused Patch Merging upsampling layer for local context enhancement; and (4) an extra small-scale detection head to preserve fine details. On VisDrone2019, experimental results show that SDP-YOLOv8 improved [email protected] by 3.90% and [email protected]:0.95 by 2.60%, with a 14.4% reduction in parameters. The model maintains real-time performance (53.5 FPS on an RTX 3090 at FP32 with batch size 1, 38.7 FPS on a Jetson Orin Nano with TensorRT FP16 at batch size 1) and offers a favorable trade-off between detection accuracy, parameter efficiency, and memory footprint, making it a potential candidate for onboard deployment on resource-limited UAVs in aerial monitoring scenarios, pending further validation on diverse datasets and hardware platforms.