SS-YOLOv8: small-size object detection algorithm based on improved YOLOv8 for UAV imagery

Unmanned aerial vehicle (UAV) image object detection has extensive applications across both civilian and military domains. However, the traditional YOLOv8 detection algorithm faces significant challenges in detecting small objects in UAV imagery, primarily due to a high missed detection rate and an excessive number of parameters. To address these issues, this paper introduces an enhanced small object detection approach, called Small-Size Object Detection Algorithm Based on Improved YOLOv8 for UAV Imagery (SS-YOLOv8). Firstly, considering the difficulties and stringent real-time requirements in detecting small objects in UAV aerial images, this work streamlines the model by eliminating the large object detection head and its associated redundant network layers, significantly limiting the parameter count. Furthermore, a specialized tiny object detection head is proposed. It is custom-designed for detecting small objects. To enhance the model’s capacity for extracting fine features of small objects and mitigate information loss during feature extraction, the convolution module is replaced in the backbone with space-to-depth convolution (SPD-Conv). Moreover, in the neck section, the self created GCU module is incorporated as it effectively amalgamates deep semantic features with shallow positional ones. Finally, while maintaining consistent parameter costs, multi-scale features are meticulously reused and incorporated to achieve a more comprehensive and sophisticated feature fusion. The experimental results on the VisDrone2019 dataset showed that compared with YOLOv8, SS-YOLOv8 improved its MAP index by 6.9% on the validation set and 5.8% on the test set, while reducing the number of parameters and model size by 65.93% and 64.85%, respectively. Compared with the best performing comparison algorithm, SS-YOLOv8 has the highest detection accuracy, indicating the superiority of this method.