测试技术学报2025，Vol.39Issue(2)：113-120,8.DOI:10.62756/csjs.1671-7449.2025017
基于优化YOLOv7-Tiny的表情识别算法
Expression Recognition Algorithm Based on Optimized YOLOv7-Tiny
摘要
Abstract
Expression recognition can not only improve human-computer interaction experience and pro-mote the development of emotional computing,but also assist in mental health assessment and treatment,and improve social security and monitoring efficiency.To improve the detection average accuracy of expression recognition,this paper proposes an expression recognition algorithm based on improved YOLOv7-tiny.Firstly,the original activation function of YOLOv7-tiny is replaced with the Mish func-tion,which improves the optimization ability of the model.Furthermore,the CA attention mechanism is added to the backbone network of YOLOv7-tiny to improve the attention to the target area of interest and increase the average accuracy of detection.Finally,the upsampling part of the Neck layer is replaced by the lightweight upsampling operator CARAFE to improve the feature fusion capability.Experimental results show that the detection effect of the improved detection algorithm is significantly improved.Com-pared with the original YOLOv7-tiny,the detection effect of the enhanced detection algorithm is increased by 1.6 percent point to 88.6%,and that of mAP0.5:0.95 is increased by 1.3 percent point to 64%.Image detection speed reaches 5.0 ms per image and the model remains lightweight.关键词
目标检测/表情识别/YOLOv7-tiny/注意力机制/Mish函数/CARAFE算子Key words
object detection/expression recognition/YOLOv7-tiny/attention mechanism/Mish func-tion/CARAFE operator分类
临床医学引用本文复制引用
常星花,王建荣..基于优化YOLOv7-Tiny的表情识别算法[J].测试技术学报,2025,39(2):113-120,8.基金项目
中国博士后科学基金资助项目(2021M692400) （2021M692400）
山西省基础研究计划项目(202303021212360) （202303021212360）
山西省高等学校科技创新计划项目(2023L379) （2023L379）
山西省高等学校科技创新平台项目(2022P016) （2022P016）
