现代电子技术2025，Vol.48Issue(7)：119-125,7.DOI:10.16652/j.issn.1004-373x.2025.07.017
基于YOLOv7-tiny的血细胞检测算法
Blood cell detection algorithm based on YOLOv7-tiny
摘要
Abstract
The blood routine examination is vital for medical diagnosis,identifying and counting components like red blood cells(RBCs),white blood cells(WBCs)and platelets(PLTs).In view of the irregular cell shape,large change of object scale and mutual occlusion of cells in blood cell detection,a blood cell detection algorithm EMCDModel based on improved YOLOv7-tiny is proposed.Firstly,the deformable convolutional networks(DCNv3)is used to replace the 2D convolution of the efficient long-distance aggregation network,and an ELAN-DF module is proposed,which improves the learning ability of irregular object fea-tures and reduces the model parameters and computation.MPDIoU(minimum point distance based IoU)is used to replace the original CIoU(complete intersection over union)to adapt to the scale variations in the case of dense blood cell distribution,lowering missed detection rates due to occlusion.The CBAM(convolutional block attention module)attention mechanism is introduced to the backbone to enhance the learning for the key information of blood cells,so as to improve the detection accuracy of small ob-jects like PLTs.Finally,the lightweight upsampling operator CARAFE(content-aware reassembly of features)is used to replace the nearest neighbor interpolation,so as to improve the feature fusion in the neck network and reduce the model parameters.Tests on the BCCD(blood cell count and detection)dataset show that the EMCDModel achieves an mAP(mean average preci-sion)of 92.8%with a model size of 5.5 MB.In comparison with the YOLOv7-tiny,the EMCDModel improves mAP by 3.8%,re-duces parameters by 8.15%,and improves blood cell detection accuracy effectively.关键词
深度学习/血细胞检测/YOLOv7-tiny/注意力机制/可变形卷积/小目标检测Key words
deep learning/blood cell detection/YOLOv7-tiny/attention mechanism/deformable convolution/small object detection分类
信息技术与安全科学引用本文复制引用
叶鑫,钟国韵,刘梅锋..基于YOLOv7-tiny的血细胞检测算法[J].现代电子技术,2025,48(7):119-125,7.基金项目
国家自然科学基金项目(62162002) （62162002）
江西省主要学科学术和技术带头人领军人才项目(20225BCJ22004) （20225BCJ22004）
