{"title":"WeldNet:用于焊接缺陷识别的轻量级深度学习模型","authors":"Rongdi Wang, Hao Wang, Zhenhao He, Jianchao Zhu, Haiqiang Zuo","doi":"10.1007/s40194-024-01759-9","DOIUrl":null,"url":null,"abstract":"<div><p>Weld defect detection is an important task in the welding process. Although there are many excellent weld defect detection models, there is still much room for improvement in stability and accuracy. In this study, a lightweight deep learning model called WeldNet is proposed to improve the existing weld defect recognition network for its poor generalization performance, overfitting, and large memory occupation, using a design with a small number of parameters but with better performance. We also proposed an ensemble-distillation strategy in the training process, which effectively improved the accuracy rate and proposed an improved model ensemble scheme. The experimental results show that the final designed WeldNet model performs well in detecting weld defects and achieves state-of-the-art performance. Its number of parameters is only 26.8% of that of ResNet18, but the accuracy is 8.9% higher, while achieving a 24.2 ms inference time on CPU to meet the demand of real-time operation. The study is of guiding significance for solving practical problems in weld defect detection, and provides new ideas for the application of deep learning in industry. The code used in this article is available at https://github.com/Wanglaoban3/WeldNet.git.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"68 11","pages":"2963 - 2974"},"PeriodicalIF":2.4000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WeldNet: a lightweight deep learning model for welding defect recognition\",\"authors\":\"Rongdi Wang, Hao Wang, Zhenhao He, Jianchao Zhu, Haiqiang Zuo\",\"doi\":\"10.1007/s40194-024-01759-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Weld defect detection is an important task in the welding process. Although there are many excellent weld defect detection models, there is still much room for improvement in stability and accuracy. In this study, a lightweight deep learning model called WeldNet is proposed to improve the existing weld defect recognition network for its poor generalization performance, overfitting, and large memory occupation, using a design with a small number of parameters but with better performance. We also proposed an ensemble-distillation strategy in the training process, which effectively improved the accuracy rate and proposed an improved model ensemble scheme. The experimental results show that the final designed WeldNet model performs well in detecting weld defects and achieves state-of-the-art performance. Its number of parameters is only 26.8% of that of ResNet18, but the accuracy is 8.9% higher, while achieving a 24.2 ms inference time on CPU to meet the demand of real-time operation. The study is of guiding significance for solving practical problems in weld defect detection, and provides new ideas for the application of deep learning in industry. The code used in this article is available at https://github.com/Wanglaoban3/WeldNet.git.</p></div>\",\"PeriodicalId\":809,\"journal\":{\"name\":\"Welding in the World\",\"volume\":\"68 11\",\"pages\":\"2963 - 2974\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Welding in the World\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s40194-024-01759-9\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Welding in the World","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s40194-024-01759-9","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
摘要
焊接缺陷检测是焊接过程中的一项重要任务。虽然有很多优秀的焊接缺陷检测模型,但在稳定性和准确性方面仍有很大的改进空间。本研究提出了一种名为 WeldNet 的轻量级深度学习模型,以改进现有焊接缺陷识别网络泛化性能差、过拟合、内存占用大等问题,采用参数数量少但性能更好的设计。我们还提出了训练过程中的集合-蒸馏策略,有效提高了准确率,并提出了改进的模型集合方案。实验结果表明,最终设计的 WeldNet 模型在检测焊接缺陷方面表现良好,达到了最先进的性能。其参数数仅为 ResNet18 的 26.8%,但准确率却提高了 8.9%,同时在 CPU 上实现了 24.2 ms 的推理时间,满足了实时运行的需求。该研究对解决焊接缺陷检测中的实际问题具有指导意义,为深度学习在工业领域的应用提供了新思路。本文使用的代码可在 https://github.com/Wanglaoban3/WeldNet.git 上获取。
WeldNet: a lightweight deep learning model for welding defect recognition
Weld defect detection is an important task in the welding process. Although there are many excellent weld defect detection models, there is still much room for improvement in stability and accuracy. In this study, a lightweight deep learning model called WeldNet is proposed to improve the existing weld defect recognition network for its poor generalization performance, overfitting, and large memory occupation, using a design with a small number of parameters but with better performance. We also proposed an ensemble-distillation strategy in the training process, which effectively improved the accuracy rate and proposed an improved model ensemble scheme. The experimental results show that the final designed WeldNet model performs well in detecting weld defects and achieves state-of-the-art performance. Its number of parameters is only 26.8% of that of ResNet18, but the accuracy is 8.9% higher, while achieving a 24.2 ms inference time on CPU to meet the demand of real-time operation. The study is of guiding significance for solving practical problems in weld defect detection, and provides new ideas for the application of deep learning in industry. The code used in this article is available at https://github.com/Wanglaoban3/WeldNet.git.
期刊介绍:
The journal Welding in the World publishes authoritative papers on every aspect of materials joining, including welding, brazing, soldering, cutting, thermal spraying and allied joining and fabrication techniques.