Algorithm design for food-picking combining deep learning and biometrics recognition

Abstract

Irregular-shape food picking and processing is a common problem in industrial automation,which can be difficult for classical image processing techniques,because of the big variations in food shape and characteristics,also the high-performance requirements in both algorithm accuracy and speed.In this paper,a hybrid method based on deep learning and feature recognition was proposed,which first roughly localized the target point based on deep learning model,and then created a search range accordingly.After that,based on biological feature analysis,target point could be accurately localized in the search range.Based on the data of shrimps,a kind of common food,the performance of the proposed method was tested.The shrimp images were pre-processed and used to train the deep learning model for rough localization.Then the shrimp body pose was normalized for edge extraction after proper rotation and projection.The extracted edge curve in search range was analyzed to accurately localize the target joint point.The validation results based on a test set including 1000 samples prove the feasibility of the method – the final detection rate of the proposed hybrid method is 97%.The performance meets industrial requirements on this case.

Keywords

• computer vision;food picking;deep learning;feature recognition