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Blood cell detection is a typical small-scale object detection problem incomputer vision. In this paper, we propose a CST-YOLO model for blood celldetection based on YOLOv7 architecture and enhance it with the CNN-SwinTransformer (CST), which is a new attempt at CNN-Transformer fusion. We alsointroduce three other useful modules: Weighted Efficient Layer AggregationNetworks (W-ELAN), Multiscale Channel Split (MCS), and ConcatenateConvolutional Layers (CatConv) in our CST-YOLO to improve small-scale objectdetection precision. Experimental results show that the proposed CST-YOLOachieves 92.7, 95.6, and 91.1 mAP@0.5 respectively on three blood celldatasets, outperforming state-of-the-art object detectors, e.g., YOLOv5 andYOLOv7. Our code is available at https://github.com/mkang315/CST-YOLO.

CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer