Vascular Intervention Guidewire Detection Algorithm Based on Improved YOLOv5m
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.
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