摘要
绿叶蔬菜的智能包装加工是实现绿叶蔬菜智能化生产、降低生产成本的重要部分,对绿叶蔬菜在包装加工时的杂质检测是其重要前提。以青菜为研究对象,提出一种基于Mask R-CNN的青菜杂质检测模型。首先采集标注掺杂枯树叶、枯菜叶和碎纸片3种常见杂质的青菜图像1370多张,并通过数据增强的方法扩充建立含有2740张青菜杂质图像的数据集。为减少背景对杂质检测的影响,通过在Mask R-CNN模型中加入协调注意力机制,同时添加全连接层和Dropout层,增强模型特征提取能力,减少过拟合现象,并使用迁移学习方法对模型进行微调。结果表明改进后的Mask R-CNN算法对青菜杂质识别的平均精度均值为99.19%,检测速度为8.45 FPS,检测效果良好,可以满足青菜杂质的检测需求。
The intelligent packaging and processing of green leafy vegetables is an important part of realizing intelligent production of green leafy vegetables and reducing production costs.The detection of impurities in the packaging of green leafy vegetables is an important prerequisite.Taking vegetables as the research object,this paper proposes a vegetable impurity detection model based on Mask R-CNN.Firstly,more than 1370 cabbage images were collected and labeled with 3 kinds of common impurities,including withered leaves,withered leaves and shredded paper.The data set containing 2740 cabbage impurity images was expanded by the method of data enhancement.In order to reduce the influence of background on impurity detection,this paper add a coordinated attention mechanism,a fully connected layer and Dropout layer to the Mask R-CNN model,reduce over fitting and fine tune the model using transfer learning methods.The results show that the average accuracy of the improved Mask R-CNN algorithm for the identification of vegetable impurities is 99.19%,the detection speed is 8.45 FPS,and the detection effect is good,which can meet the detection requirements of vegetable impurities.
作者
赵爽
俞永强
苗玉彬
刘可心
Zhao Shuang;Yu Yongqiang;Miao Yubin;Liu Kexin(School of Machinery,Shanghai Dianji University,Shanghai,201100,China;School of Mechanical and Power Engineering,Shanghai Jiaotong University,Shanghai,201100,China)
出处
《中国农机化学报》
北大核心
2024年第9期77-82,140,共7页
Journal of Chinese Agricultural Mechanization
基金
国家自然科学基金项目(51975361)
上海市科技兴农项目(沪农科创字(2019)第2—2号)。
