SPDC-YOLO: An Efficient Small Target Detection Network Based on Improved YOLOv8 for Drone Aerial Image

Abstract

Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. However, images captured by UAVs usually have multi-scale features, complex backgrounds, uneven illumination, and low target resolution, which makes target detection in UAV images very challenging. To tackle these challenges, this paper introduces SPDC-YOLO, a novel model built upon YOLOv8. In the backbone, the model eliminates the last C2f module and the final downsampling module, thus avoiding the loss of small target features. In the neck, this paper proposes a novel feature pyramid, SPC-FPN, which employs the SBA (Selective Boundary Aggregation) module to fuse features from two distinct scales. In the head, the P5 detection head is eliminated, and a new detection head, Dyhead-DCNv4, is proposed, replacing DCNv2 in the original Dyhead with DCNv4 and utilizing three attention mechanisms for dynamic feature weighting. In addition, the model uses the CGB (Context Guided Block) module for downsampling, which can learn and fuse local features with surrounding contextual information, and the PPA (Parallelized Patch-Aware Attention) module replacing the original C2f module to further improve feature expression capability. Finally, SPDC-YOLO adopts EIoU as the loss function to optimize target localization accuracy. On the public dataset VisDrone2019, the experimental results show that SPDC-YOLO improves mAP50 by 3.4% compared to YOLOv8n while reducing the parameters count by 1.03 M. Compared with other related methods, SPDC-YOLO demonstrates better performance.