{"title":"AIoT-enabled defect detection with minimal data: A few-shot learning approach combining prototypical and relational networks for smart manufacturing","authors":"Chih-Cheng Chen , Hsien-Yang Liao , Chun-You Liu","doi":"10.1016/j.iot.2024.101327","DOIUrl":null,"url":null,"abstract":"<div><p>Defect detection is crucial in manufacturing processes but traditional AI-based algorithms require large datasets for accurate results. For new or customized products, the number of images with detected defects is limited. Therefore, we developed a few-shot learning approach integrating a prototypical and relation network (PRN), algorithms with meta-learning, and the Artificial Internet of Things (AIoT). For rapid defect detection with IoT sensors, such minimal data are used for a smart manufacturing ecosystem., making it ideal for dynamic production environments. We tested the AIOT-enhanced PRN on two datasets using the following data augmentation methods: random rotation and horizontal translation (RH), random rotation and vertical translation (RV), and horizontal and vertical translation (HV). The developed PRN efficiently learned from minimal data to reduce the occurrence of overfitting issues in the MVTec 3D-AD dataset which are caused by a limited number of defect sample images. When testing the AIOT-enhanced PRN with the NEU-DET dataset, accuracies in 5-way 5-shot settings using RV, RH, 15° rotation, and HV were 100 %. Under Gaussian noise, the AIOT-enhanced PRN showed an accuracy of 100 % in 5-way 5-shot and 5-way 1-shot scenarios using HV. For salt-and-pepper noise, the accuracy of the AIOT-enhanced PRN ranged from 98.49 to 99.04 %. The developed AIOT-enhanced PRN improved defect detection accuracy and real-time monitoring capability with minimal data. The developed AIOT-enhanced PRN can be used for efficient and flexible product quality control in Industry 4.0.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"27 ","pages":"Article 101327"},"PeriodicalIF":6.0000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660524002683","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Defect detection is crucial in manufacturing processes but traditional AI-based algorithms require large datasets for accurate results. For new or customized products, the number of images with detected defects is limited. Therefore, we developed a few-shot learning approach integrating a prototypical and relation network (PRN), algorithms with meta-learning, and the Artificial Internet of Things (AIoT). For rapid defect detection with IoT sensors, such minimal data are used for a smart manufacturing ecosystem., making it ideal for dynamic production environments. We tested the AIOT-enhanced PRN on two datasets using the following data augmentation methods: random rotation and horizontal translation (RH), random rotation and vertical translation (RV), and horizontal and vertical translation (HV). The developed PRN efficiently learned from minimal data to reduce the occurrence of overfitting issues in the MVTec 3D-AD dataset which are caused by a limited number of defect sample images. When testing the AIOT-enhanced PRN with the NEU-DET dataset, accuracies in 5-way 5-shot settings using RV, RH, 15° rotation, and HV were 100 %. Under Gaussian noise, the AIOT-enhanced PRN showed an accuracy of 100 % in 5-way 5-shot and 5-way 1-shot scenarios using HV. For salt-and-pepper noise, the accuracy of the AIOT-enhanced PRN ranged from 98.49 to 99.04 %. The developed AIOT-enhanced PRN improved defect detection accuracy and real-time monitoring capability with minimal data. The developed AIOT-enhanced PRN can be used for efficient and flexible product quality control in Industry 4.0.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.