CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved
  YOLOv7 and CNN-Swin Transformer
Ming Kang Chee-Ming Ting Fung Fung Ting Raphaël Phan
Blood cell detection is a typical small-scale object detection problem in
computer vision. In this paper, we propose a CST-YOLO model for blood cell
detection based on YOLOv7 architecture and enhance it with the CNN-Swin
Transformer (CST), which is a new attempt at CNN-Transformer fusion. We also
introduce three other useful modules: Weighted Efficient Layer Aggregation
Networks (W-ELAN), Multiscale Channel Split (MCS), and Concatenate
Convolutional Layers (CatConv) in our CST-YOLO to improve small-scale object
detection precision. Experimental results show that the proposed CST-YOLO
achieves 92.7, 95.6, and 91.1 mAP@0.5 respectively on three blood cell
datasets, outperforming state-of-the-art object detectors, e.g., YOLOv5 and
YOLOv7. Our code is available at https://github.com/mkang315/CST-YOLO.