包装与食品机械2024，Vol.42Issue(6)：65-72,8.DOI:10.3969/j.issn.1005-1295.2024.06.009
基于YOLOv5s网络的常见菜品识别检测
Common dish identification and detection based on YOLOv5s network
摘要
Abstract
In order to solve the problem of manual settlement and low efficiency of canteen and restaurant dishes,several types of improved algorithms were proposed based on the YOLOv5s network model,among which the accuracy,recall and average accuracy of YOLOv5s,YOLOv5s-C2f,YOLOv5s-SE,YOLOv5s-MobileNetV3 and YOLOv5x algorithms were 83%,88.6%,89.4%;94.9%,79.1%,87.6%;91.6%,76%,84.9%;88.3%,94.9%,81.5%;93.6%,99.4%,99.4%,respectively;and the detection time of each image was 0.36,0.29,0.34,0.23,and 0.98 s,respectively.Experimental results show that compared with the YOLOv5s algorithm,the average accuracy of the YOLOv5s-MobileNetV3 algorithm is reduced by 7.9%,and the detection time is reduced by 36.12%.The average accuracy of the YOLOv5s-C2f algorithm is reduced by 1.8%,and the detection time is reduced by 19.44%.The average accuracy of the YOLOv5x algorithm is increased by 10%,and the detection time is increased by 63.27%.The YOLOv5s-MobileNetV3 algorithm maintains accuracy while greatly reducing the detection time,effectively achieving a balance between fast detection and performance.The YOLOv5x algorithm has a high accuracy rate and is suitable for applications where high accuracy is required.The research provides technical support for smart food services.关键词
深度学习/C2f模块/通道注意力机制/YOLOv5s/菜品识别Key words
deep learning/C2f module/channel attention mechanism/YOLOv5s/dish identification分类
轻工纺织引用本文复制引用
季旭,宋垲,冯怡然..基于YOLOv5s网络的常见菜品识别检测[J].包装与食品机械,2024,42(6):65-72,8.基金项目
国家重点研发计划项目(2018YFD0400800) （2018YFD0400800）
辽宁省教育厅科研项目(JYTMS20230395) （JYTMS20230395）
