Aluminum alloy piston material metallographic structure recognition method and system based on neural network

Abstract

This invention discloses a method and system for metallographic structure identification of aluminum alloy piston materials based on neural networks, comprising: acquiring a preprocessed image, performing pixel-level segmentation, and generating a set of candidate defect regions; extracting morphological feature vectors, boundary feature vectors, and context feature vectors for each candidate region; fusing the morphological feature vectors, boundary feature vectors, and context feature vectors to construct a comprehensive discrimination feature vector, and classifying and discriminating each candidate region to output the identification results of pores or inclusions; mapping the classification results back to the corresponding region in the pixel-level segmentation probability map to generate a spatial distribution map of pores and inclusions, and performing structural statistical analysis based on the distribution map to output porosity and inclusion distribution information; this invention solves the problems of inaccurate detection of small or complex-shaped defects, imprecise statistical analysis of the spatial distribution of pores and inclusions, and high cost of high-precision annotation.

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