Multiclass small target detection algorithm for surface defects of chemicals special steel

IF 1.9 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Frontiers in Physics Pub Date : 2024-08-30 DOI:10.3389/fphy.2024.1451165
Yuanyuan Wang, Shaofeng Yan, Hauwa Suleiman Abdullahi, Shangbing Gao, Haiyan Zhang, Xiuchuan Chen, Hu Zhao
{"title":"Multiclass small target detection algorithm for surface defects of chemicals special steel","authors":"Yuanyuan Wang, Shaofeng Yan, Hauwa Suleiman Abdullahi, Shangbing Gao, Haiyan Zhang, Xiuchuan Chen, Hu Zhao","doi":"10.3389/fphy.2024.1451165","DOIUrl":null,"url":null,"abstract":"Introduction: Chemical special steels are widely used in chemical equipment manufacturing and other fields, and small defects on its surface (such as cracks and punches) are easy to cause serious accidents in harsh environments.Methods: In order to solve this problem, this paper proposes an improved defect detection algorithm for chemical special steel based on YOLOv8. Firstly, in order to effectively capture local and global information, a ParC2Net (Parallel-C2f) structure is proposed for feature extraction, which can accurately capture the subtle features of steel defects. Secondly, the loss function is adjusted to MPD-IOU, and its dynamic non-monotonic focusing characteristics are used to effectively solve the overfitting problem of the bounding box of low-quality targets. In addition, RepGFPN is used to fuse multi-scale features, deepen the interaction between semantics and spatial information, and significantly improve the efficiency of cross-layer information transmission. Finally, the RexSE-Head (ResNeXt-Squeeze-Excitation) design is adopted to enhance the positioning accuracy of small defect targets.Results and discussion: The experimental results show that the mAP@0.5 of the improved model reaches 93.5%, and the number of parameters is only 3.29M, which realizes the high precision and high response performance of the detection of small defects in chemical special steels, and highlights the practical application value of the model. The code is available at <jats:ext-link>https://github.com/improvment/prs-yolo</jats:ext-link>.","PeriodicalId":12507,"journal":{"name":"Frontiers in Physics","volume":"1 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3389/fphy.2024.1451165","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction: Chemical special steels are widely used in chemical equipment manufacturing and other fields, and small defects on its surface (such as cracks and punches) are easy to cause serious accidents in harsh environments.Methods: In order to solve this problem, this paper proposes an improved defect detection algorithm for chemical special steel based on YOLOv8. Firstly, in order to effectively capture local and global information, a ParC2Net (Parallel-C2f) structure is proposed for feature extraction, which can accurately capture the subtle features of steel defects. Secondly, the loss function is adjusted to MPD-IOU, and its dynamic non-monotonic focusing characteristics are used to effectively solve the overfitting problem of the bounding box of low-quality targets. In addition, RepGFPN is used to fuse multi-scale features, deepen the interaction between semantics and spatial information, and significantly improve the efficiency of cross-layer information transmission. Finally, the RexSE-Head (ResNeXt-Squeeze-Excitation) design is adopted to enhance the positioning accuracy of small defect targets.Results and discussion: The experimental results show that the mAP@0.5 of the improved model reaches 93.5%, and the number of parameters is only 3.29M, which realizes the high precision and high response performance of the detection of small defects in chemical special steels, and highlights the practical application value of the model. The code is available at https://github.com/improvment/prs-yolo.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
化学品特种钢表面缺陷的多类小目标检测算法
简介:化工特种钢广泛应用于化工设备制造等领域,其表面的微小缺陷(如裂纹、冲孔等)在恶劣环境下容易引发严重事故:针对这一问题,本文提出了一种基于 YOLOv8 的改进型化学特殊钢缺陷检测算法。首先,为了有效捕捉局部和全局信息,提出了一种用于特征提取的 ParC2Net(Parallel-C2f)结构,它能准确捕捉钢材缺陷的细微特征。其次,将损失函数调整为 MPD-IOU,利用其动态非单调聚焦特性,有效解决了低质量目标边界框的过拟合问题。此外,利用 RepGFPN 融合多尺度特征,深化语义与空间信息的交互,显著提高跨层信息传输效率。最后,采用RexSE-Head(ResNeXt-Squeeze-Excitation)设计,提高了小缺陷目标的定位精度。结果与讨论:实验结果表明,改进模型的mAP@0.5,达到了93.5%,参数数仅为3.29M,实现了化学特种钢中小缺陷检测的高精度和高响应性能,凸显了模型的实际应用价值。代码见 https://github.com/improvment/prs-yolo。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Frontiers in Physics
Frontiers in Physics Mathematics-Mathematical Physics
CiteScore
4.50
自引率
6.50%
发文量
1215
审稿时长
12 weeks
期刊介绍: Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.
期刊最新文献
Intelligent diagnostic method for developmental hip dislocation Bonner sphere measurements of high-energy neutron spectra from a 1 GeV/u 56Fe ion beam on an aluminum target and comparison to spectra obtained by Monte Carlo simulations Comparative analysis of the influence of different shapes of shaft sections on dust transportation Detection of natural pulse waves (PWs) in 3D using high frame rate imaging for anisotropy characterization Tunable continuous wave Yb:CaWO4 laser operating in NIR spectral region
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1