应用掩模 R-CNN 机器学习算法分割陶瓷铸铜模具的电子显微镜图像

IF 2.6 1区 地球科学 Q1 ANTHROPOLOGY
Lingyu Liao , Zhenfei Sun , Siran Liu , Shining Ma , Kunlong Chen , Yue Liu , Yongtian Wang , Weitao Song
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引用次数: 0

摘要

铸造模具的材料特征对于了解中国青铜时代青铜礼器生产的演变和多样化至关重要。在相关研究中,通常采用背散射电子(BSE)图像探测器来分析铸模的微观结构,从而有效地揭示粘土基质、粉砂颗粒和空隙的体积比和形状特征。由于 BSE 图像通常包含许多形状极不规则的相,因此对这些图像进行定量分析和交叉比较始终是一项挑战。传统的方法是使用耗时的人工点计数或多步骤图像处理来获得半定量结果。针对这些挑战,我们提出了一种名为 BCM-SegNet 的深度学习方法,这是一种基于 Mask R-CNN 的优化算法,用于分割青铜铸造模具和型芯的 BSE 图像。使用所提出的方法,即使对于背景复杂的图像,也能根据良好的分割结果提供关键参数,如分割颗粒的面积、Feret 直径、圆度和坚实度。实验结果表明,该算法的分割精度高达 95%,准确率约为 91%,显示了其强大的泛化能力。这项研究为考古陶瓷材料的微观特征分析、颗粒分类和考古研究中的工艺流程判断奠定了重要基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying a mask R-CNN machine learning algorithm for segmenting electron microscope images of ceramic bronze-casting moulds

Material characteristics of casting moulds are crucial for understanding the evolution and diversification of bronze ritual vessel production in Bronze Age China. During relevant studies, a Back Scattered Electron (BSE) image detector is commonly employed to analyze mould microstructure, effectively revealing the volume ratios and shape features of the clay matrix, silt/sand particles, and voids. It is always challenging to analyze and cross-compare these BSE images quantitatively since they typically contain numerous phases with highly irregular shapes. Traditionally, time consuming manual point counting or multi-step image processing were used to obtain semi-quantitative results. Addressing these challenges, we have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. Using the proposed method, key parameters, such as area, Feret diameter, roundness, and solidity of segmented particles, can be provided based on well segmented results, even for the images with complex background. Experimental outcomes show that the algorithm achieves a segmentation precision of 95% and an accuracy of around 91%, demonstrating its strong generalization capability. This study provides a significant foundation for micro-feature analysis of archaeological ceramic materials, classification of particles, and determination of technological processes in archaeological research.

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来源期刊
Journal of Archaeological Science
Journal of Archaeological Science 地学-地球科学综合
CiteScore
6.10
自引率
7.10%
发文量
112
审稿时长
49 days
期刊介绍: The Journal of Archaeological Science is aimed at archaeologists and scientists with particular interests in advancing the development and application of scientific techniques and methodologies to all areas of archaeology. This established monthly journal publishes focus articles, original research papers and major review articles, of wide archaeological significance. The journal provides an international forum for archaeologists and scientists from widely different scientific backgrounds who share a common interest in developing and applying scientific methods to inform major debates through improving the quality and reliability of scientific information derived from archaeological research.
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