Ming Guo, Li Zhu, Youshan Zhao, Xingyu Tang, Kecai Guo, Yanru Shi, Liping Han
{"title":"基于近距离图像点云和红外图像的液化天然气外罐表面裂缝智能提取技术","authors":"Ming Guo, Li Zhu, Youshan Zhao, Xingyu Tang, Kecai Guo, Yanru Shi, Liping Han","doi":"10.1007/s10921-024-01103-7","DOIUrl":null,"url":null,"abstract":"<p>Most of the studies on oil tanks have focused on the analysis of deformation and settlement, and more research needs to be done on crack extraction from external LNG tanks.</p><p>Oil tanks are more sensitive to temperature due to the lower temperature inside the tank. Using infrared images as a dataset for crack recognition can identify cracks that the naked eye cannot see, and a convolutional neural network that introduces a channel attention mechanism is used for crack recognition with a recognition accuracy of 85.9%.</p><p>The automatic extraction of three-dimensional (3D) crack point clouds using depth images is novel and accurate, with an accuracy of about 97.6%.</p>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery\",\"authors\":\"Ming Guo, Li Zhu, Youshan Zhao, Xingyu Tang, Kecai Guo, Yanru Shi, Liping Han\",\"doi\":\"10.1007/s10921-024-01103-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Most of the studies on oil tanks have focused on the analysis of deformation and settlement, and more research needs to be done on crack extraction from external LNG tanks.</p><p>Oil tanks are more sensitive to temperature due to the lower temperature inside the tank. Using infrared images as a dataset for crack recognition can identify cracks that the naked eye cannot see, and a convolutional neural network that introduces a channel attention mechanism is used for crack recognition with a recognition accuracy of 85.9%.</p><p>The automatic extraction of three-dimensional (3D) crack point clouds using depth images is novel and accurate, with an accuracy of about 97.6%.</p>\",\"PeriodicalId\":655,\"journal\":{\"name\":\"Journal of Nondestructive Evaluation\",\"volume\":\"43 3\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nondestructive Evaluation\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10921-024-01103-7\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10921-024-01103-7","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery
Most of the studies on oil tanks have focused on the analysis of deformation and settlement, and more research needs to be done on crack extraction from external LNG tanks.
Oil tanks are more sensitive to temperature due to the lower temperature inside the tank. Using infrared images as a dataset for crack recognition can identify cracks that the naked eye cannot see, and a convolutional neural network that introduces a channel attention mechanism is used for crack recognition with a recognition accuracy of 85.9%.
The automatic extraction of three-dimensional (3D) crack point clouds using depth images is novel and accurate, with an accuracy of about 97.6%.
期刊介绍：
Journal of Nondestructive Evaluation provides a forum for the broad range of scientific and engineering activities involved in developing a quantitative nondestructive evaluation (NDE) capability. This interdisciplinary journal publishes papers on the development of new equipment, analyses, and approaches to nondestructive measurements.
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