食品与机械2026，Vol.42Issue(2)：74-81,8.DOI:10.13652/j.spjx.1003.5788.2025.60180
基于改进YOLOv13和X射线的包装食品内异物智能检测方法
Intelligent detection method for foreign objects in packaged foods based on improved YOLOv13 and X-ray
摘要
Abstract
[Objective]To address problems in X-ray-based foreign object detection in packaged foods,including low accuracy caused by the high proportion of low-frequency features in images and small grayscale differences between foreign objects and the background,as well as the loss of micro foreign object features,an improved intelligent detection scheme is proposed to overcome the inability of traditional detection methods to meet the real-time and reliability requirements of automated production lines.[Methods]Based on the intelligent detection system for foreign objects in packaged foods,this study proposes an intelligent detection method for foreign objects in packaged foods integrating an improved YOLOv13 model and X-ray.The core feature extraction module,DC-A2C2F,is redesigned to precisely focus on regions with sharp gray-level variations along foreign object boundaries,thus enhancing the discriminability between foreign object features and the background.A multi-order feature aggregation module(MFAM)is introduced into the backbone network to mitigate the dilution of micro foreign object features and retain critical detail information.A dual-path fusion pyramid network(DFPN)is designed to optimize the neck structure,achieving balanced matching between semantic and detailed information to accommodate the detection requirements of foreign objects of different sizes.[Results]The proposed method achieves a mean average precision of over 98.50%for multiple types of foreign objects,including metal,glass,and plastic.Compared with the YOLOv13 model,the proposed method reduces the missed detection rate from 2.2%to 0.4%while maintaining an inference speed of over 50 frames per second.[Conclusion]The improved YOLOv13 model accurately adapts to the feature characteristics of X-ray images.The proposed detection method combines high detection accuracy,a low missed detection rate,and real-time performance,fully meeting the real-time detection requirements of automated production lines.关键词
包装食品/异物检测/YOLOv13模型/X射线/智能化检测系统Key words
packaged food/foreign object detection/YOLOv13 model/X-ray/intelligent detection system引用本文复制引用
郭德超,刘子志,张豪,赵强..基于改进YOLOv13和X射线的包装食品内异物智能检测方法[J].食品与机械,2026,42(2):74-81,8.基金项目
广东省教育厅科研项目计划课题(编号:21GZJY675032) （编号:21GZJY675032）
广东省中医药健康服务与产业发展研究中心项目(编号:2025YBA14,2025YBA05) （编号:2025YBA14,2025YBA05）
广州市哲学社科规划课题(编号:2024GZGJ272,2023GZGJ64) （编号:2024GZGJ272,2023GZGJ64）
