摘要
目的设计基于多角度像特征的果蔬识别方案。方法采用多角度特征的果蔬识别算法,在水平、垂直、多角度旋转等Haar-like特征的基础上与AdaBoost自学习算法充分结合。通过离线训练,获得识别西红柿的AdaBoost分类器,在此基础上以平均像素值为核心创造颜色特征分类器,使Haar-like与AdaBoost分类器有机结合,实现对果蔬类型的自动识别。结果以西红柿为例进行识别时,准确性超过95%,且该方法对干扰因素具有较强的抗性,完成一帧图像的识别只需85 ms。结论该方法能够迅速的完成识别任务,达到了实时性方面的要求。
Objective To design a fruit and vegetable recognition scheme based on multi-angle image features.Methods The fruit and vegetable recognition algorithm based on multi-angle features was combined with AdaBoost self-learning algorithm on the basis of horizontal,vertical and multi-angle rotation Haar-Like features.Through offline training,an AdaBoost classifier for tomato recognition was obtained.On this basis,a color feature classifier was created based on the average pixel value.The Haar-like classifier and AdaBoost classifier were combined organously to realize automatic recognition of fruit and vegetable types.Results When tomatoes were used as an example,the accuracy was more than 95%,and the method had strong resistance to interference factors,it only took 85 ms to complete the recognition of one frame of image.Conclusion This method can quickly complete the recognition task,which meets the real-time requirements.
作者
李辉熠
乔波
LI Hui-Yi;QIAO Bo(Hunan Mass Media Vocational and Technical College,Changsha 410100,China;College of Information and Intelligent Science and Technology,Hunan Agricultural University,Changsha 410128,China)
出处
《食品安全质量检测学报》
CAS
北大核心
2021年第10期4129-4135,共7页
Journal of Food Safety and Quality
基金
湖南省教育厅科学研究重点项目(20A249)。
