食品与机械2024，Vol.40Issue(5)：128-136,9.DOI:10.13652/j.spjx.1003.5788.2023.80986
基于改进Alexnet的轻量化香蕉成熟度检测
Lightweight banana ripeness detection based on improved Alexnet
摘要
Abstract
Objective:To obtain a lightweight Mini-Alexnet banana ripeness grading model and apply it to Android mobile devices.Methods:Based on the external characteristics of bananas with different ripeness,the Alexnet network model was restructured,part of the convolutional layer was deleted,and the global average pooling was used instead of the full connection layer to reduce the model parameters and required memory.A larger convolutional kernel was replaced to extract the global characteristics of the banana skin to achieve an improved lightweight Mini-Alexnet network model.Then the Mini-Alexnet network model was deployed as Android mobile APP,and its feasibility and practicability were verified.Results:The Mini-Alexnet model was only 11.6 MB,and the identification accuracy rate of banana ripeness level 5 was 97.76％.The accuracy rate of local picture recognition mode,photo recognition mode and real-time recognition mode of the mobile APP banana ripeness automatic identification system was 86.66％,79.33％and 74.00％,respectively,with an average accuracy rate of 80％.Conclusion:The improved Mini-Alexnet model occupies less memory space.关键词
香蕉/轻量化模型/成熟度/移动设备/APPKey words
bananas/lightweight model/ripeness/mobile device/APP引用本文复制引用
蒋瑜,王灵敏..基于改进Alexnet的轻量化香蕉成熟度检测[J].食品与机械,2024,40(5):128-136,9.基金项目
广西高校中青年教师科研基础能力提升项目(编号:2024KY1247,2020KY36006) （编号:2024KY1247,2020KY36006）
