GRAF: Gap region aware framework for Al-Si alloy microscopic image segmentation

Abstract

The shape of the primary silicon in Al-Si alloys is one of the main factors determining the properties of the alloy, which makes the microscopic image of Al-Si alloys become a crucial reference material in the study of the alloy. Accurate segmentation of silicon particles in Al-Si alloy micrographs is a necessary prerequisite for their quantitative analysis. The traditional manual annotation method requires high time and labor costs and will be affected by subjective factors of the annotator. Although automatic image segmentation methods can avoid these problems, there are silicon particles of different shapes and sizes in the Al-Si alloy microscopic image, and it is hard to separate adjacent silicon particles. These characteristics bring the automatic segmentation method severe challenge. Therefore, to address the issue of deviation in the annotation range when there is agglomeration of primary silicon particles in the microstructure images of Al-Si alloys, We propose a Residual Position-Aware Attention Network (RPAA-Net) for the segmentation of primary silicon particles. The RPAA-Net method enhances segmentation accuracy and robustness by incorporating a residual position-aware attention mechanism, enabling the network to better distinguish silicon particles from the surrounding non-silicon regions. To address the challenge of separating adjacent silicon particles in the image, we introduce a novel Gap Region Weighting Strategy (GRWS). GRWS enhances the model's focus on the gap regions between adjacent objects by applying additional weights during training. By combining RPAA-Net with GRWS, we construct a Gap Region Awareness Framework (GRAF) and experiment with different weights. We find that the weight with the most significant impact is 10, leading to the framework being named GRAF-10. Finally, we apply GRAF-10 to the Aluminum-Silicon Alloy Microstructure (ASAM) dataset. The results show that GRAF-10 outperforms the current state-of-the-art methods, improving the object-level Dice index(Dobj) by 1.56%. Additionally, it reduces the Merge Error (ME) metric by 6.74 and the Variation of Information (VI) metric by 5.43, effectively segmenting aluminum–silicon alloy microstructure images.