Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading

TL;DR

This paper introduces GSNets, a deep learning classification method with guided self-attention for automated steel grain size analysis, achieving high accuracy and potential for broader image analysis tasks.

Contribution

The paper presents a novel guided self-attention module and triple-stream merging technique that enhance feature representation and model generalization in grain size classification.

Findings

Achieved 90.1% classification accuracy, surpassing state-of-the-art methods.

Introduced a guided self-attention module for better relational feature capture.

Demonstrated potential applicability to object detection and semantic segmentation.

Abstract

With the development of steel materials, metallographic analysis has become increasingly important. Unfortunately, grain size analysis is a manual process that requires experts to evaluate metallographic photographs, which is unreliable and time-consuming. To resolve this problem, we propose a novel classifi-cation method based on deep learning, namely GSNets, a family of hybrid models which can effectively introduce guided self-attention for classifying grain size. Concretely, we build our models from three insights:(1) Introducing our novel guided self-attention module can assist the model in finding the generalized necessarily distinct vectors capable of retaining intricate rela-tional connections and rich local feature information; (2) By improving the pixel-wise linear independence of the feature map, the highly condensed semantic representation will be captured by the model; (3) Our novel triple-stream merging module can significantly improve the generalization capability and efficiency of the model. Experiments show that our GSNet yields a classifi-cation accuracy of 90.1%, surpassing the state-of-the-art Swin Transformer V2 by 1.9% on the steel grain size dataset, which comprises 3,599 images with 14 grain size levels. Furthermore, we intuitively believe our approach is applicable to broader ap-plications like object detection and semantic segmentation.