{"title":"基于 Unet-CBAM 网络的脉冲热成像无损检测","authors":"Chenghao Wu, Dan Wu, Pengfei Zhu","doi":"10.3389/fphy.2024.1458194","DOIUrl":null,"url":null,"abstract":"Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.","PeriodicalId":12507,"journal":{"name":"Frontiers in Physics","volume":"12 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-destructive testing based on Unet-CBAM network for pulsed thermography\",\"authors\":\"Chenghao Wu, Dan Wu, Pengfei Zhu\",\"doi\":\"10.3389/fphy.2024.1458194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.\",\"PeriodicalId\":12507,\"journal\":{\"name\":\"Frontiers in Physics\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-18\",\"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.2024.1458194\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3389/fphy.2024.1458194","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}
Non-destructive testing based on Unet-CBAM network for pulsed thermography
Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.
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.