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.