{"title":"Detection of small foreign objects in Pu-erh sun-dried green tea: An enhanced YOLOv8 neural network model based on deep learning","authors":"","doi":"10.1016/j.foodcont.2024.110890","DOIUrl":null,"url":null,"abstract":"<div><div>To efficiently and accurately detect minuscule foreign objects in the processing of Pu-erh sun-dried green tea, ensuring food quality and consumer safety, this study innovatively proposes an enhanced YOLOv8 neural network based on deep learning. In light of the shortcomings of the traditional YOLOv8 network, to further enhance the model's ability to identify foreign object targets, improve the depth and breadth of feature extraction, and to better understand the contextual connections between different parts of the image, this study employs a Shape-IoU optimized loss function. It replaces parts of the network structure with Receptive-Field Attention Convolution technology and embeds Double Attention Networks to optimize the network. Experimental results show that the enhanced YOLOv8 neural network model achieves a precision rate of 98.35% for the detection of foreign objects in Pu-erh sun-dried green tea, which is a 3.93% increase compared to the original YOLOv8 network. Compared to mainstream detection models such as YOLOv7, YOLOv5, Faster-RCNN, CornerNet, and SSD, the mean Average Precision values of the enhanced YOLOv8 network model have significantly increased by 4.48%, 6.66%, 13.63%, 13.20%, and 9.84% respectively. This enhanced YOLOv8 network provides a viable research method and significant reference for the detection of small foreign objects in Pu-erh sun-dried green tea, holding substantial importance for tea-producing enterprises and food safety regulatory authorities. Furthermore, this study offers a more comprehensive and efficient solution for foreign object detection in Pu-erh tea and the broader food industry, laying the groundwork for the modernization and intelligentization of food safety and quality control.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Control","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0956713524006078","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
To efficiently and accurately detect minuscule foreign objects in the processing of Pu-erh sun-dried green tea, ensuring food quality and consumer safety, this study innovatively proposes an enhanced YOLOv8 neural network based on deep learning. In light of the shortcomings of the traditional YOLOv8 network, to further enhance the model's ability to identify foreign object targets, improve the depth and breadth of feature extraction, and to better understand the contextual connections between different parts of the image, this study employs a Shape-IoU optimized loss function. It replaces parts of the network structure with Receptive-Field Attention Convolution technology and embeds Double Attention Networks to optimize the network. Experimental results show that the enhanced YOLOv8 neural network model achieves a precision rate of 98.35% for the detection of foreign objects in Pu-erh sun-dried green tea, which is a 3.93% increase compared to the original YOLOv8 network. Compared to mainstream detection models such as YOLOv7, YOLOv5, Faster-RCNN, CornerNet, and SSD, the mean Average Precision values of the enhanced YOLOv8 network model have significantly increased by 4.48%, 6.66%, 13.63%, 13.20%, and 9.84% respectively. This enhanced YOLOv8 network provides a viable research method and significant reference for the detection of small foreign objects in Pu-erh sun-dried green tea, holding substantial importance for tea-producing enterprises and food safety regulatory authorities. Furthermore, this study offers a more comprehensive and efficient solution for foreign object detection in Pu-erh tea and the broader food industry, laying the groundwork for the modernization and intelligentization of food safety and quality control.
期刊介绍:
Food Control is an international journal that provides essential information for those involved in food safety and process control.
Food Control covers the below areas that relate to food process control or to food safety of human foods:
• Microbial food safety and antimicrobial systems
• Mycotoxins
• Hazard analysis, HACCP and food safety objectives
• Risk assessment, including microbial and chemical hazards
• Quality assurance
• Good manufacturing practices
• Food process systems design and control
• Food Packaging technology and materials in contact with foods
• Rapid methods of analysis and detection, including sensor technology
• Codes of practice, legislation and international harmonization
• Consumer issues
• Education, training and research needs.
The scope of Food Control is comprehensive and includes original research papers, authoritative reviews, short communications, comment articles that report on new developments in food control, and position papers.