软件导刊2024，Vol.23Issue(10)：199-206,8.DOI:10.11907/rjdk.232010
基于改进YOLOv5m的血管介入导丝检测算法
Vascular Intervention Guidewire Detection Algorithm Based on Improved YOLOv5m
鲍芳嘉 1张明伟 1张天逸 1程云章1
作者信息
- 1. 上海理工大学 健康科学与工程学院||上海介入医疗器械工程技术研究中心,上海 200093
- 折叠
摘要
Abstract
In order to solve the problems of current mainstream target detection algorithms being less used in minimally invasive vascular in-terventional guidewire detection,low detection accuracy and slow detection speed,an improved YOLOv5m network is proposed for the detec-tion of vascular interventional guidewires.First,deformable convolution is introduced into the backbone network of YOLOv5m to replace some standard convolutions,and a coordinate attention mechanism is added to the CSP module of the backbone network;BiFPN is used in the neck to performs feature fusion improving the model's ability to fuse different feature layers.Experimental results show that the mAP@0.5 of the im-proved YOLOv5m algorithm reaches 87.8%,which is 5.7%higher than YOLOv5m,indicating that this algorithm has relatively high applica-tion value in vascular interventional guidewire detection.关键词
血管介入/YOLOv5m/导丝检测/可变形卷积/坐标注意力机制/BiFPNKey words
vascular intervention/YOLOv5m/guidewire detection/deformable convolution/coordinate attention mechanism/BiFPN分类
信息技术与安全科学引用本文复制引用
鲍芳嘉,张明伟,张天逸,程云章..基于改进YOLOv5m的血管介入导丝检测算法[J].软件导刊,2024,23(10):199-206,8.
