Similarity comparison of scrap based on Siamese neural network
Addressing the challenges of the heavy workload involved in scrap steel classification and the lack of uniform grading standards,this paper employs machine learning to identify and compare scrap steel images for determining the scrap steel grade.A scrap steel classification dataset is con-structed,and the Siamese neural network is utilized to train the dataset,selecting the optimal weights to enable the model to accurately distinguish different types of scrap steel.The similarity between the scrap steel benchmark and the image to be tested is compared using the Siamese network calculation method.Based on the similarity results,the scrap steel grade is determined.When the similarity ap-proaches 1,the scrap steel shapes are considered similar.When the similarity approaches 0,the shapes are deemed different,allowing for the determination of the distribution of scrap steel types in the image.Experimental results demonstrate that the method of scrap steel classification using similar-ity comparison and the Siamese neural network exhibits excellent accuracy and reliability.Compared with traditional manual classification methods,this approach not only significantly improves classifica-tion efficiency but also achieves standardization and consistency in scrap steel grading.
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