Leukocyte detection is difficult due to different shapes and degrees of staining of leukocytes during real detection process in complex scenarios. To solve the problem， a dual feature fusion CenterNet based leukocyte detection method TFF-CenterNet （Twice-Fusion-Feature CenterNet） was proposed. Firstly， the features of the backbone network were fused with the features of deconvolution layers through Feature Pyramid Network （FPN）. In this way， the feature extraction ability of the method was improved to solve the problems of individual differences and different degrees of staining of leukocytes. Then， aiming at the problem of severe imbalance between the image area of leukocytes and the background image area， the heatmap loss function was improved to enhance the focus on positive samples of leukocyte and improve detection mean Average Precision （mAP）. Finally， for the characteristics of the tiny target， random location， and cell adhesion of leukocyte images， coordinate attention and coordinate convolution were introduced to improve the attention and sensitivity of leukocyte location information. For leukocytes in complex scenarios， TFF-CenterNet achieves the mAP of 97.01% and the detection speed of 167 frame/s， which are 3.24 percentage points higher and 42 frame/s faster than those of CenterNet respectively. Experimental results show that the proposed method can improve the mAP of leukocyte detection in complex situations while achieving real-time requirements， and improves the robustness， so that this method can provide technical support for rapid automatic leukocyte detection in complementary medical diagnosis.