# Boundary Learning by Using Weighted Propagation in Convolution Network

## TL;DR

This paper introduces WPU-Net, a novel convolutional neural network that enhances boundary detection in microscopic images of poly-crystalline materials by incorporating weighted propagation and adaptive boundary weights, significantly improving accuracy.

## Contribution

The paper presents a new boundary detection network with spatial consistency and adaptive pixel weighting, advancing image segmentation in materials science.

## Key findings

- Reduces boundary detection error rate by 7%
- Outperforms existing methods significantly
- Provides a new dataset for materials image processing

## Abstract

In material science, image segmentation is of great significance for quantitative analysis of microstructures. Here, we propose a novel Weighted Propagation Convolution Neural Network based on U-Net (WPU-Net) to detect boundary in poly-crystalline microscopic images. We introduce spatial consistency into network to eliminate the defects in raw microscopic image. And we customize adaptive boundary weight for each pixel in each grain, so that it leads the network to preserve grain's geometric and topological characteristics. Moreover, we provide our dataset with the goal of advancing the development of image processing in materials science. Experiments demonstrate that the proposed method achieves promising performance in both of objective and subjective assessment. In boundary detection task, it reduces the error rate by 7\%, which outperforms state-of-the-art methods by a large margin.