SSA-YOLO: An Improved YOLO for Hot-Rolled Strip Steel Surface Defect Detection

IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-10-30 DOI:10.1109/TIM.2024.3488136
Xiaohua Huang;Jiahao Zhu;Ying Huo
{"title":"SSA-YOLO: An Improved YOLO for Hot-Rolled Strip Steel Surface Defect Detection","authors":"Xiaohua Huang;Jiahao Zhu;Ying Huo","doi":"10.1109/TIM.2024.3488136","DOIUrl":null,"url":null,"abstract":"In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at \n<uri>https://github.com/MIPIT-Team/SSA-YOLO</uri>\n.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-17"},"PeriodicalIF":5.6000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10739337/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at https://github.com/MIPIT-Team/SSA-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
SSA-YOLO:用于热轧带钢表面缺陷检测的改进型 YOLO
在热轧带钢的生产过程中,各种机械力和环境条件都可能导致表面缺陷,因此,检测这些缺陷对于确保高质量的产品生产和避免行业的重大经济损失至关重要。然而,目前常用于钢材表面缺陷检测的 "只看一次(YOLO)"系列模型效果有限。在本文中,我们提出了 YOLO 的改进版本,即通过卷积挤压激发(CSE)模块、带斯温变换器(CST)模块的 Conv2d-BatchNorm-SiLU (CBS) 和自适应空间特征融合(ASFF)检测头模块(即 SSA-YOLO)增强的 YOLO,专门用于端到端表面缺陷检测。我们的方法包含几项旨在提高性能的关键修改。首先,我们在骨干网的浅层卷积网络模块中集成了信道关注机制模块。这一改进侧重于通道信息,以改进与小缺陷相关的特征提取,同时减少候选盒中的冗余信息。此外,我们还在颈部融合了斯温变换器(Swin-T)模块,以增强检测多样化和多尺度缺陷的特征表示。最后,我们在 YOLO 中引入了 ASFF,以增加特征金字塔网络(FPN)中高低层次之间的交叉互动。实验结果表明,与其他最先进的模型相比,我们的 SSA-YOLO 模型性能优越、效果显著。我们的方法在检测表面缺陷方面实现了更高的准确性和灵敏度,在钢带生产质量控制方面取得了显著进步。代码见 https://github.com/MIPIT-Team/SSA-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
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
期刊最新文献
Front Cover A Lightweight Reprogramming Framework for Cross-Device Fault Diagnosis in Edge Computing First Arrival Picking of Aircraft-Excited Seismic Waves Based on Energy Distribution DMPDD-Net: An Effective Defect Detection Method for Aluminum Profiles Surface Defect C-DHV: A Cascaded Deep Hough Voting-Based Tracking Algorithm for LiDAR Point Clouds
×
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