Supermarket Fruit and Vegetable Retrieval Method Based on Deep Learning
In view of the problems that the current settlement method of supermarket fruits and vegetables cannot add new catego-ries and low accuracy of small sample recognition,this paper proposes a supermarket fruits and vegetables retrieval method based on deep learning.The method obtains fruit and vegetable subjects through YOLOv4 to remove redundant background infor-mation,and extracts corresponding deep semantic features of fruit and vegetable subjects through MobileNetV3.Finally,cat-egory judgment is completed according to metric learning technology.This paper conducts experiments in accordance with the ac-tual operation conditions of supermarkets and concludes that the method could accurately identify different fruit and vegetable cat-egories under the condition of small samples.When the number of samples for each category is 15,the average recognition rate is about 94%,the time cost is 0.93s,and the new categories could be updated in real time.This method greatly reduces the huge la-bor and time cost in the actual operation of traditional supermarkets,and provides a solution for the realization of intelligence and automation in the fruit and vegetable retail industry.
image retrievalfruit and vegetable recognitioncategory increasesmall sample recognition
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