DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery

Unmanned aerial vehicle (UAV) imagery often suffers from significant object scale variations, high target density, and varying distances due to shooting conditions and environmental factors, leading to reduced robustness and low detection accuracy in conventional models. To address these issues, this study adopts DGBL-YOLOv8s, an improved object detection model tailored for UAV perspectives based on YOLOv8s. First, a Dilated Wide Residual (DWR) module is introduced to replace the C2f module in the backbone network of YOLOv8, enhancing the model’s capability to capture fine-grained features and contextual information. Second, the neck structure is redesigned by incorporating a Global-to-Local Spatial Aggregation (GLSA) module combined with a Bidirectional Feature Pyramid Network (BiFPN), which strengthens feature fusion. Third, a lightweight shared convolution detection head is proposed, incorporating shared convolution and batch normalization techniques. Additionally, to further improve small object detection, a dedicated small-object detection head is introduced. Results from experiments on the VisDrone dataset reveal that DGBL-YOLOv8s enhances detection accuracy by 8.5% relative to the baseline model, alongside a 34.8% reduction in parameter count. The overall performance exceeds most of the current detection models, which confirms the advantages of the proposed improvement.