Detection of stacked medical devices using improved YOLOv5s
10.3969/j.issn.1005-202X.2025.02.012
- VernacularTitle:改进YOLOv5s的堆叠医疗器械检测算法
- Author:
Changrui TIAN
1
;
Wei LIAO
;
Zhen XU
Author Information
1. 上海工程技术大学电子电气工程学院,上海 201620
- Publication Type:Journal Article
- Keywords:
medical device;
YOLOv5s;
attention mechanism;
α-DIOU;
deep learning
- From:
Chinese Journal of Medical Physics
2025;42(2):220-226
- CountryChina
- Language:Chinese
-
Abstract:
A medical device detection method based on improved YOLOv5s was proposed to solve the problem of medical device stacking and further improve the detection accuracy of medical devices.The proposed method uses C2f module to optimize YOLOv5s network for improving the detection accuracy,introduces squeeze-and-excitation network into the feature fusion network for improving the model's attention to effective information,and constructs α-DIOU by introducing Alpha intersection union ratio(α-IOU)on the basis of the distance-intersection over union(DIOU)loss function,which makes the bounding box regression more accurate and enables the accurate detection of medical devices in the image.Experimental results show that the improved model has precision,recall rate and mean average precision of 81.8%,93.7%and 91.5%,respectively,for medical device detection on the validation set,which are 3.2%,3.4%and 4.6%higher than YOLOv5s model.The proposed method is simple and effective,and is expected to provide new ideas for the detection methods of medical devices.
