Blood count is a common clinical test. Aiming at the low accuracy of the existing blood cell detection methods due to the uneven, dense and mutual occlusion of blood cells in blood microscope images, an improved YOLOX blood cell detection algorithm is proposed. The algorithm firstly addes Focal loss into the loss function to improve the imbalance of positive and negative samples and uneven cell types in the single-stage target detection algorithm; then, a mixed attention mechanism is added into the residual module, which reduces the probability of missed detection and false detection caused by the mutual occlusion of blood cells; then an adaptive spatial feature fusion module is added at the end of the feature fusion to improve the feature expression ability; finally, an inverse depth separable convolution module is added into the residual module to reduce model parameters and slightly improve detection precision. The proposed algorithm is tested on the BCCD blood cell data set, and the detection accuracy of the improved YOLOX algorithm on the blood cell data set is 92.5%, which is 2.4% higher than that of the YOLOX algorithm, and the model parameters are reduced by 8%; The detection accuracy of the algorithm on the COCO2017 general data set is 41.7%, which is 1.2% higher than that of the original YOLOX algorithm.