SIG-YOLOv8s-based small object detection algorithm for UAV imagery via multilevel feature fusion

Abstract

Abstract. In the domain of unmanned aerial vehicle (UAV) aerial imagery, objects frequently exhibit dense and nonuniform distribution patterns, often resulting in false positives and missed detections. To overcome these challenges, we propose SIG-YOLOv8s, an advanced object detection architecture built upon the YOLOv8s framework. The nomenclature reflects three core enhancements integrated into the model: the small object detection head, the Inner-DS-IoU loss, and the feature gather-and-distribute (FGD) module. First, the FGD module is utilized to process multiscale feature information via dilated convolutions. By optimizing cross-layer recursive information fusion, this module effectively mitigates the adverse effects of varying shooting angles and complex backgrounds. Second, the Inner-DS-IoU loss function is employed to account for bounding box shape and scale during regression, thereby enhancing robustness against complex environments and accelerating convergence. In addition, a dedicated prediction head for small objects is incorporated to capture tiny targets that are typically challenging to discern, significantly improving detection accuracy. Finally, extensive empirical evaluations validate the efficacy of the proposed method, demonstrating that SIG-YOLOv8s achieves superior performance in object detection and recognition tasks.