中国计量大学学报2024，Vol.35Issue(2)：333-340,356,9.DOI:10.3969/j.issn.2096-2835.2024.02.017
基于改进YOLOv7的吸烟行为识别算法研究
Research on smoking behavior recognition algorithms based on improved YOLOv7
摘要
Abstract
Aims:A smoking behavior recognition method based on improved YOLOv7 was proposed to improve the efficiency and accuracy of artificial intelligence in smoking behavior recognition.Methods:Based on the YOLOv7 algorithm,the GhostNet network structure was used to replace its backbone network,reducing the number of network model parameters and computational complexity.The CBAM attention mechanism was introduced to improve the effectiveness of feature extraction.A multi-scale feature fusion module and an improved loss function were incorporated to enhance the model's detection performance in complex environments.Results:Testing conducted on a smoking dataset showed that the improved model reduced the number of parameters and computational complexity by 16.6% and 37.4% ,respectively.The detection speed was improved to 103.4 F/s;and the accuracy was improved by 2.8% .Conclusions:The proposed lightweight network model can meet the requirements of real-time video monitoring and can achieve real-time detection on low-power embedded devices.关键词
吸烟行为/YOLOv7/轻量化网络/注意力机制/多尺度特征融合/损失函数Key words
smoking behavior/YOLOv7/lightweight network/attention mechanism/multi-scale feature fusion/loss function分类
信息技术与安全科学引用本文复制引用
梁皖,柯海森,李孝禄..基于改进YOLOv7的吸烟行为识别算法研究[J].中国计量大学学报,2024,35(2):333-340,356,9.基金项目
浙江省科技计划项目(No.2023C01163) （No.2023C01163）
