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
6/26/2023
cs.CVeess.SPstat.AP

Official code: mkang315/CST-YOLO (75 stars, GPL-3.0, last commit 2025-12-15).

Abstract

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% [email protected], respectively, on three blood cell datasets, outperforming state-of-the-art object detectors, e.g., RT-DETR, YOLOv5, and YOLOv7. Our code is available at https://github.com/mkang315/CST-YOLO.

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Code Implementations(1)

mkang315/CST-YOLOOfficial100%
GPL-3.0

[ICIP'24 Lecture Presentation] Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".

7519Shell, PythonJun 26, 202310 months agoGPL-3.0
blood-cell-counting-datasetblood-cell-detectionblood-cell-detection-datasetcomplete-bloodcell-countcomputer-vision+15 more

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Cite this paper

@article{kang2023cstyolo,
  title  = {CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer},
  author = {Ming Kang and Chee-Ming Ting and Fung Fung Ting and Raphaël Phan},
  year   = {2023},
  eprint = {2306.14590},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14590v2}
}

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