{"title":"RDT-FragNet:用于智能岩石碎块识别和粒度分布采集的 DCN 变压器网络","authors":"","doi":"10.1016/j.compgeo.2024.106809","DOIUrl":null,"url":null,"abstract":"<div><div>Accurately and promptly identifying rock fragments and particle size distribution after blasting is crucial for rock transportation and aggregate control in hydraulic and hydropower engineering. Manual screening and traditional edge detection methods suffer from subjectivity and inefficiency, resulting in considerable processing time. Images of rock fragments post-blasting, captured in open-air conditions, present challenges due to overlapping fragments, complicating intelligent recognition. To address this, an instance segmentation model, RDT-FragNet, is designed for rock fragment segmentation. RDT-FragNet is a hybrid model that integrates the Deformable Convolutional Network (DCN) and the Transformer Attention Mechanism (TAM). The DCN-Transformer structure adaptively preserves global and local features, enhancing the segmentation and recognition of rock fragment edges. Comparative analyses and rigorous ablation studies demonstrate RDT-FragNet’s competitive advantages. RDT-FragNet outperforms other advanced models in both quantitative metrics and visual results. The visualization results and the characteristic and maximum particle size of rock fragments closely match the actual situation. The robustness and applicability of the RDT-FragNet model are validated using images from two additional engineering projects. This research introduces an intelligent, efficient, and objective method for rock fragment analysis in open-air settings.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RDT-FragNet: A DCN-Transformer network for intelligent rock fragment recognition and particle size distribution acquisition\",\"authors\":\"\",\"doi\":\"10.1016/j.compgeo.2024.106809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurately and promptly identifying rock fragments and particle size distribution after blasting is crucial for rock transportation and aggregate control in hydraulic and hydropower engineering. Manual screening and traditional edge detection methods suffer from subjectivity and inefficiency, resulting in considerable processing time. Images of rock fragments post-blasting, captured in open-air conditions, present challenges due to overlapping fragments, complicating intelligent recognition. To address this, an instance segmentation model, RDT-FragNet, is designed for rock fragment segmentation. RDT-FragNet is a hybrid model that integrates the Deformable Convolutional Network (DCN) and the Transformer Attention Mechanism (TAM). The DCN-Transformer structure adaptively preserves global and local features, enhancing the segmentation and recognition of rock fragment edges. Comparative analyses and rigorous ablation studies demonstrate RDT-FragNet’s competitive advantages. RDT-FragNet outperforms other advanced models in both quantitative metrics and visual results. The visualization results and the characteristic and maximum particle size of rock fragments closely match the actual situation. The robustness and applicability of the RDT-FragNet model are validated using images from two additional engineering projects. This research introduces an intelligent, efficient, and objective method for rock fragment analysis in open-air settings.</div></div>\",\"PeriodicalId\":55217,\"journal\":{\"name\":\"Computers and Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0266352X24007481\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266352X24007481","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
RDT-FragNet: A DCN-Transformer network for intelligent rock fragment recognition and particle size distribution acquisition
Accurately and promptly identifying rock fragments and particle size distribution after blasting is crucial for rock transportation and aggregate control in hydraulic and hydropower engineering. Manual screening and traditional edge detection methods suffer from subjectivity and inefficiency, resulting in considerable processing time. Images of rock fragments post-blasting, captured in open-air conditions, present challenges due to overlapping fragments, complicating intelligent recognition. To address this, an instance segmentation model, RDT-FragNet, is designed for rock fragment segmentation. RDT-FragNet is a hybrid model that integrates the Deformable Convolutional Network (DCN) and the Transformer Attention Mechanism (TAM). The DCN-Transformer structure adaptively preserves global and local features, enhancing the segmentation and recognition of rock fragment edges. Comparative analyses and rigorous ablation studies demonstrate RDT-FragNet’s competitive advantages. RDT-FragNet outperforms other advanced models in both quantitative metrics and visual results. The visualization results and the characteristic and maximum particle size of rock fragments closely match the actual situation. The robustness and applicability of the RDT-FragNet model are validated using images from two additional engineering projects. This research introduces an intelligent, efficient, and objective method for rock fragment analysis in open-air settings.
期刊介绍:
The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.