Composite material microstructure defect analysis method and system based on artificial intelligence image recognition

Abstract

This application relates to a method and system for analyzing microstructural defects in composite materials based on artificial intelligence image recognition. The method includes: acquiring microstructural images of composite material samples; performing multi-scale feature extraction on the microstructural images to obtain a multi-level feature map set; inputting the multi-level feature map set into a preset defect analysis model to obtain a defect segmentation map of the composite material sample; wherein the defect analysis model includes: a topology-aware feature fusion module for analyzing the topological features of each convolutional feature map to obtain topological attribute data of the convolutional feature maps, and fusing the convolutional feature maps based on the topological attribute data to obtain a fused feature map; a morphological constraint decision module connected to the topology-aware feature fusion module for transforming the fused feature map based on preset morphological rule constraint parameters to obtain a defect segmentation map. This method can improve the accuracy and efficiency of defect analysis in composite materials.

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