包装与食品机械2025，Vol.43Issue(6)：10-18,9.DOI:10.3969/j.issn.1005-1295.2025.06.002
面向边缘计算的轻量化菜品识别方法研究
Research on lightweight dish recognition method for edge computing
摘要
Abstract
To address the issues of slow inference speed,large parameter count,and difficulty in deploying to resource-constrained edge computing devices in existing dish recognition algorithms,this paper proposes a lightweight MD-YOLOv11 model based on YOLOv11n.By utilizing the MobileNetV4 fused backbone network,the feature extraction of the backbone is accelerated.A streamlined pyramid network is designed,constructing a lightweight C3k2-SNv2 module to improve the efficiency of neck feature fusion.The detection head structure is simplified to enhance inference speed.The MPDIoU loss function is introduced to improve the model's bounding box convergence speed and detection accuracy.Experimental results show that the MD-YOLOv11 model achieves a mean average precision(mAP@0.5)of 98.55%on the dish dataset.When deployed on the same raspberry pi edge computing device,compared to the baseline model,MD-YOLOv11 reduces the detection time by 64.58%,the parameter count by 54.83%,and the computational complexity by 73.85%while maintaining accuracy,meeting the practical requirements for real-time dish detection.This research provides a reference for real-time dish recognition methods and system development oriented towards edge computing.关键词
深度学习/YOLOv11/菜品识别/轻量化/边缘计算Key words
deep learning/YOLOv11/dish recognition/lightweight/edge computing分类
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孙畅,刘海隆,宋梦微,杨锦民..面向边缘计算的轻量化菜品识别方法研究[J].包装与食品机械,2025,43(6):10-18,9.基金项目
国家重点研发计划项目(2025YFE0102800) （2025YFE0102800）
新疆维吾尔自治区科技计划项目(2025E01030) （2025E01030）
