The observation and counting of red blood cells, white blood cells, and platelets in the blood are an important basis for clinical medical diagnosis. Abnormal blood cells mean that there may be blood-related problems such as clotting abnormalities, infections, and inflammation. As artificial blood cell detection is not only labor-intensive but also prone to false detection and misses, a novel blood cell detection algorithm YOLOv5-CBF is proposed to address the above problem. On the basis of the YOLOv5 framework, the algorithm improves detection accuracy by adding a coordinate attention (CA) mechanism to the backbone network. The FPN+PAN structure in the neck network is changed to the feature fusion structure combining the idea of the bidirectional feature pyramid network (BiFPN), a cross-scale feature fusion method; in this way, the multi-scale features of the target can be effectively fused. In addition to the three-scale detection, a small target detection layer is added to improve the identification accuracy of small target platelets in the dataset. The results of a large number of experiments conducted on the dataset BCCD show that the algorithm presents an average accuracy improvement of 2.7% in the detection of the three blood cells compared to the conventional YOLOv5 algorithm, demonstrating good performance. The algorithm is highly practical for blood cell detection.