CT理论与应用研究2025，Vol.34Issue(4)：560-570,11.DOI:10.15953/j.ctta.2025.066
基于深度学习的面食异物检测方法
A Method for Detecting Foreign Objects in Pastries Based on Deep Learning
摘要
Abstract
During the industrial production of pastries,foreign substances such as plastic and rubber can accidentally enter the processing chain,posing serious risks to consumer health and safety.Therefore,detecting foreign substances in pastries is a critical quality control step.X-ray computed tomography(CT)is a fast,non-contact,and non-destructive testing method that is widely used in quality inspection processes on of industrial pastry production lines.However,owing to the high-throughput detection requirements of such production lines,the analysis of a single product typically needs to be completed within 1s.This limited time frame makes it impossible to capture a sufficient number of projection images,restricting the use of conventional CT methods.In this study,we propose a foreign-object detection method based on the U-Net network,which is trained using CT data from the same type of samples and foreign objects.The experimental results show that this method requires only a few projection images to accurately identify multiple foreign objects.It can quickly and efficiently detect foreign objects from CT data on industrial production lines,greatly improving detection efficiency in the pastry industry.关键词
深度学习/CT/异物检测Key words
deep learning/CT/foreign object detection分类
信息技术与安全科学引用本文复制引用
唐浩奇,杨君,陈荣昌..基于深度学习的面食异物检测方法[J].CT理论与应用研究,2025,34(4):560-570,11.基金项目
国家重点研发计划课题(2022YFA1604002) （2022YFA1604002）
广东特支计划(2023TQ07Z464) （2023TQ07Z464）
国家自然科学基金(12405366). （12405366）
