Lingyu Liao , Zhenfei Sun , Siran Liu , Shining Ma , Kunlong Chen , Yue Liu , Yongtian Wang , Weitao Song
{"title":"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","authors":"Lingyu Liao , Zhenfei Sun , Siran Liu , Shining Ma , Kunlong Chen , Yue Liu , Yongtian Wang , Weitao Song","doi":"10.1016/j.jas.2024.106049","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":50254,"journal":{"name":"Journal of Archaeological Science","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applying a mask R-CNN machine learning algorithm for segmenting electron microscope images of ceramic bronze-casting moulds\",\"authors\":\"Lingyu Liao , Zhenfei Sun , Siran Liu , Shining Ma , Kunlong Chen , Yue Liu , Yongtian Wang , Weitao Song\",\"doi\":\"10.1016/j.jas.2024.106049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":50254,\"journal\":{\"name\":\"Journal of Archaeological Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Archaeological Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305440324001171\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANTHROPOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Archaeological Science","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305440324001171","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANTHROPOLOGY","Score":null,"Total":0}
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.
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.