To solve the problem of detecting erythrocytes and spirochaete in blood, an improved YOLOV7 algorithm is proposed that integrates the attention mechanism, optimization loss function and NMS module, called SE-YOLOv7. This method used Normalized Gaussian Wasserstein Distance to optimize the original loss function, enhancing the model's detection performance for erythrocytes and spirochaete. Use soft-NMS instead of the NMS module to alleviate the network's detection problem of erythrocytes overlap. The addition of Bi-Level Routing Attention enhances the model's flexibility in computational allocation and content awareness. Experiments on DMID-ESD using Faster RCNN, SSD, Retinanet, YOLOV7 and SE-YOLOv7. Experimental results show that SE-YOLOv7 has a better detection effect, and its mAP is increased by 2.3% compared to YOLOv7. This is of great significance for achieving accurate detection of erythrocytes and spirochaete.