Intelligent detection of fastener defects and mixed assortments

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express Pub Date : 2024-08-01 DOI:10.1016/j.icte.2024.05.006

引用次数: 0

Abstract

This paper investigates the use of artificial intelligence (AI) image detection and discrimination technology to address issues related to mixed assortments and defects encountered in the fastener manufacturing and packaging processes. The defect detection system primarily utilizes the YOLOv4-tiny model with parameter setting and data augmentation techniques. The mixed assortments detection system is constructed using U-Net-Light and Siamese network. The research results demonstrate that the developed systems can indeed replace or assist on-site personnel in conducting efficient and accurate inspections and screenings.

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智能检测紧固件缺陷和混合组件

本文研究了如何利用人工智能（AI）图像检测和判别技术来解决紧固件生产和包装过程中遇到的混装和缺陷问题。缺陷检测系统主要利用 YOLOv4-tiny 模型以及参数设置和数据增强技术。混合分类检测系统是利用 U-Net-Light 和连体网络构建的。研究结果表明，所开发的系统确实可以替代或协助现场人员进行高效、准确的检查和筛选。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.