Construction of an efficient blood cell detection model for small targets and overlapping areas

To improve the detection accuracy and efficiency of small targets, overlapping targets, and complex backgrounds in blood cell images, this paper proposes an improved model based on the YOLOv12n lightweight architecture. The model integrates three core modules: the A2C2f_DYT module introduces a dynamic nonlinear mechanism and multi-head attention to enhance feature representation; the C2BRA module adopts dual-layer routing attention to achieve multi-scale semantic fusion; and the DySample module enhances the restoration of fine-grained spatial information through dynamic point sampling. Comparative experiments conducted on the standard blood cell dataset show that the improved model achieves high detection accuracy (mAP@0.5 of 97.1%) while maintaining low computational complexity (6.0 GFLOPS) and a lightweight parameter size (2.79M), outperforming mainstream models such as YOLOv8n, YOLOv10n, and YOLOv12n. Furthermore, the model demonstrates excellent accuracy and recall rates in multi-class detection, including WBCs, RBCs, and platelets, showcasing superior performance in small target detection and class adaptability. Ablation experiments further validate the positive contribution of each module to detection performance. This study presents a blood cell detection solution with high accuracy, a lightweight design, and strong robustness, making it suitable for deployment on edge devices and in intelligent medical scenarios.