Acupuncture Needle Small Object Detection Algorithm Based on Improved YOLOv5
- VernacularTitle:基于改进YOLOv5的针灸针小目标检测算法研究
- Author:
Jingqiao LU
1
;
Fangqian WAN
;
Hengcong LI
;
Yiqiao WANG
;
Chuanchi WANG
;
Jingqing HU
Author Information
- Publication Type:Journal Article
- Keywords: Acupuncture; Acupuncture needle small object detection; Deep learning; YOLOv5; Small object
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2025;27(1):202-210
- CountryChina
- Language:Chinese
- Abstract: With the scientific and modernization of acupuncture,various kinds of acupuncture medical equipment continue to innovate,especially with the emergence of intelligent acupuncture diagnosis and treatment units,automatic detection of acupuncture needles in the"needle retention"stage of acupuncture clinical practice has become a hot demand.Aiming at the problems that the input image size is too large,the acupuncture needles are slender,and the acupuncture needles are densely distributed,the Acupuncture Needle Object Detection Model(ANODM),an improved YOLOv5 model for acupuncture needles,is proposed in this paper.① In the preprocessing stage,the image is divided into multiple patches for prediction,respectively.② At the model structure level,a new small object detection layer is added to the original three detection layers to improve the recognition ability of small objects.The ordinary convolution of the backbone network is replaced by the dialated convolution to increase the sensitivity field.Features of different stages are fused.③ In the post-processing stage,Soft-NMS is used to reduce the miss rate of positive samples,and cosine similarity match is used to reduce the error rate of negative samples.The experimental results show that,compared with the original YOLOv5,the detection accuracy of the improved YOLOv5 in this paper is improved by 4.2%on the acupuncture needle small object dataset,which can better complete the acupuncture needle small target detection task.
