基于多尺度特征融合的皮肤烧伤创面分级算法  

Skin burn wound classification algorithm based onmulti-scale feature fusion

作　　者：韩旭晖 刘宇 何圭波 宋年秀 Han Xuhui;Liu Yu;He Guibo;Song Nianxiu(College of Automotive and Mechanical Engineering,Qingdao University of Technology,Qingdao 266525,China)

机构地区：[1]青岛理工大学机械与汽车工程学院,青岛266525

出　　处：《电子测量技术》2022年第18期114-118,共5页Electronic Measurement Technology

基　　金：山东省重点研发计划项目(2019GGX101038)资助。

摘　　要：为实现遭受重大火灾等灾害后,对伤员皮肤烧伤自动化分级,加快诊断效率,提出提出一种用于皮肤烧伤分类的轻量化模型BI-YOLOv5算法。替换Swish激活函数,提高模型收敛能力及检测效率;使用K-means++算法对anchors聚类分析,增强对不同尺度目标的适应能力;修改特征提取网络,提取多个尺度的特征信息,建立多尺度特征融合网络,提高模型对深层特征信息的利用率,提高小面积烧伤的识别精度。实验结果表明,BI-YOLOv5算法在检测并区分不同烧伤类别及环境干扰下烧伤检测有较高的精度和效率,mAP达到97.6,对比YOLOv5提升8.4个百分点。In order to realize the automatic classification of skin burns of the wounded after suffering a major fire and other disasters and speed up the diagnosis efficiency,a lightweight model BI-YOLOv5 algorithm for skin burn classification was proposed.Replace the Swish activation function to improve the convergence ability and detection efficiency of the model;use the K-means++algorithm to perform cluster analysis on anchors to enhance the adaptability to targets of different scales;modify the feature extraction network to extract feature information of multiple scales and establish multi-scale features The fusion network improves the utilization rate of the deep feature information by the model and improves the recognition accuracy of small-area burns.The experimental results show that the BI-YOLOv5 algorithm has high accuracy and efficiency in detecting and distinguishing different burn types and environmental disturbances,and the mAP reaches 97.6,which is 8.4 percentage points higher than that of YOLOv5.

关 键 词：深度学习 机器视觉 YOLO 火灾 皮肤烧伤 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]