Blood cell detection is a crucial task in clinical diagnosis. However, due to the diverse cell types, significant size variations, frequent target overlap, and complex backgrounds, existing detection models still face challenges in terms of accuracy and robustness. To address these issues, this paper proposes an improved YOLOv8-based object detection model, YOLO-BioFusion.The model incorporates the ACFN module to enhance the detection of small and overlapping targets. Additionally, the C2f-DPE and SPPF-LSK modules are introduced to strengthen multi-scale feature fusion and extraction, improving the model′s robustness and generalization ability. Meanwhile, the adoption of the Inner-CIoU loss function accelerates model convergence and enhances localization accuracy.Experimental results on the BCCD dataset demonstrate that YOLO-BioFusion achieves an mAP@0.5 of 94.0% and an mAP@0.5:0.95 of 65.2%, outperforming YOLOv8-n by 1.9% and 32%, respectively. Moreover, with a computational cost of only 6.8 GFLOPs, YOLO-BioFusion exhibits great potential for applications in resource-constrained environments. This study provides an efficient and accurate solution for blood cell detection in complex backgrounds.