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

2026-06-20

AI Summary

This study addresses the limitations of traditional image processing methods in analyzing precipitate phases in chromium-based alloys from electron microscopy images, which are often hindered by noise sensitivity, poor generalization, and heavy reliance on manual intervention. To overcome these challenges, the authors propose DT-SegNet, an end-to-end two-stage deep learning framework that uniquely integrates YOLOv5 for object detection with a Vision Transformer–based SegFormer model for segmentation, achieving a balance between computational efficiency and high accuracy. Experimental results demonstrate that DT-SegNet significantly outperforms mainstream tools such as Weka and ilastik across multiple metrics—including accuracy, precision, recall, and F1 score—thereby substantially enhancing the automation of precipitate identification and quantification and providing robust microstructural analysis support for high-throughput alloy development.

Abstract

The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcing phases. Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, such as Concentrated Solar Power, and their development requires efficient measurement of precipitate volume fraction and size distribution from electron microscopy images. Traditional fixed-threshold image processing is sensitive to background noise, generalises poorly across materials, and requires substantial manual measurement effort. To address these bottlenecks, this study proposes DT-SegNet, an end-to-end two-stage deep learning scheme based on YOLOv5 and SegFormer for object detection and segmentation in electron microscopy images. The approach combines the training efficiency of convolutional neural networks at the detection stage with the segmentation accuracy of a Vision Transformer. Numerical experiments show that DT-SegNet substantially outperforms state-of-the-art segmentation tools offered by Weka and ilastik across metrics including accuracy, precision, recall, and F1-score. The model provides a useful tool for alloy-development microstructure examinations and helps address the large datasets associated with high-throughput alloy development.

Problem

• precipitate measurement
• chromium-based superalloys
• electron microscopy
• microstructure analysis
• image segmentation

Innovation

• two-stage deep learning
• precipitate segmentation
• Vision Transformer
• chromium-based superalloys
• electron microscopy image analysis