The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network

Abstract

The first step in petrographic image analysis is to segment the mineral grains in the thin section petrographic image, allowing the petrographer to identify the rock based on the grain size and composition. The purpose of this research is to present an automated computer vision solution for obtaining accurate and efficient edge segmentation maps. The proposed model, termed the extinction consistency perception network, is made up of three sections. The multi-angle extinction consistency block, which is based on the extinction consistency of mineral grains, uses consecutive petrographic images to generate edge-enhanced features. Then they are processed by the multi-scale edge perception network to obtain rich expression of edge features at different levels. Afterward, a distance-map penalized compound loss function is introduced to guide the model to pay more attention to grains’ edges. The generated cross-polarized petrographic image dataset (CPPID) with meticulous annotations has been shared with the community. Experimental findings show that the proposed model is effective, which is evaluated on CPPID and scores 0.940 ODS and 0.941 OIS, outperforming seven classic edge detection models by a large margin.