Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Xia, Zhenhai; Ma, Ke; Cheng, Sibo; Blackburn, Thomas; Peng, Ziling; Zhu, Kewei; Zhang, Weihang; Xiao, Dunhui; Knowles, Alexander J.; Arcucci, Rossella

doi:10.1039/d3cp00402c

Cited by 7 publications

(3 citation statements)

References 68 publications

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“…The research presented herein utilizes the SegFormer network, which is predicated on the Transformer design paradigm, expressly conceived for the exigent task of pixel-level image segmentation. Empirical studies corroborate that the SegFormer model, in conjunction with other transformative enhancements upon the Transformer framework, plays a critical role in propelling the evolution of semantic segmentation models towards heightened efficiency [9,26].…”

Section: Introductionmentioning

confidence: 77%

“…In the realm of computer vision, the implementation of deep learning methodologies has engendered profound breakthroughs, with wide-reaching implications across multiple scientific domains, including architecture, medicine, and materials science [4][5][6][7][8][9]. Semantic segmentation, deemed a pivotal endeavor within computer vision, endeavors to delineate images and assign semantic labels to each constituent pixel, drawing upon an established taxonomy of tags [10].…”

Section: Introductionmentioning

confidence: 99%

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Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Gu,

Wang

2023

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Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework’s integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Gu,

Wang

2023

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“…Moreover, the amount of collected ground truth could not support the implementation of CNN training task. Notable examples of application of CNN are the works of Xia et al (2023), in which CNN was used to identify and measure the area of precipitate in novel chromium-based alloys [26]. Overall, many research works have sought to test and deploy models that are not computationally expensive, as in the work of Cheng et al (2022), who developed a ML model for the prediction of wildfires using satellite imagery [27].…”

Section: Data Collectionmentioning

confidence: 99%

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Giussani,

Wilczynski,

Zandonella Callegher

et al. 2024

Sustainability

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Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

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Chromium-based bcc-superalloys strengthened by iron supplements

Ma,

Blackburn,

Magnussen

et al. 2023

Acta Materialia

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Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Abstract: The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently...

Cited by 7 publications

References 68 publications

Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Chromium-based bcc-superalloys strengthened by iron supplements

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