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

IF 5.3 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers and Geotechnics Pub Date : 2024-10-10 DOI:10.1016/j.compgeo.2024.106809
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引用次数: 0

摘要

在水利水电工程中,准确及时地识别爆破后的岩石碎块和粒度分布对于岩石运输和集料控制至关重要。人工筛选和传统的边缘检测方法存在主观性和效率低下的问题,导致处理时间相当长。爆破后的岩石碎块图像是在露天条件下拍摄的,由于碎块相互重叠,给智能识别带来了挑战。为此,我们设计了一个用于岩石碎片分割的实例分割模型 RDT-FragNet。RDT-FragNet 是一种混合模型,集成了可变形卷积网络(DCN)和变压器注意机制(TAM)。DCN-Transformer 结构自适应地保留了全局和局部特征,增强了岩石碎片边缘的分割和识别能力。对比分析和严格的烧蚀研究证明了 RDT-FragNet 的竞争优势。RDT-FragNet 在定量指标和可视化结果方面都优于其他先进模型。可视化结果以及岩石碎块的特征和最大粒度与实际情况非常吻合。利用另外两个工程项目的图像验证了 RDT-FragNet 模型的稳健性和适用性。这项研究为露天环境下的岩石碎块分析引入了一种智能、高效和客观的方法。
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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.
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来源期刊
Computers and Geotechnics
Computers and Geotechnics 地学-地球科学综合
CiteScore
9.10
自引率
15.10%
发文量
438
审稿时长
45 days
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.
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