基于孪生神经网络进行废钢相似度比较  

Similarity comparison of scrap based on Siamese neural network

作　　者：胡云飞 余晋水 周小宾 彭世恒 HU Yunfei;YU Jinshui;ZHOU Xiaobin;PENG Shiheng(School of Metallurgical Engineering,Anhui University of Technology,Ma'anshan 243032,China)

机构地区：[1]安徽工业大学冶金工程学院,安徽马鞍山243032

出　　处：《冶金自动化》2024年第5期67-72,共6页Metallurgical Industry Automation

基　　金：国家自然科学基金重点项目(51704006)。

摘　　要：针对当前废钢分类工作量繁重且废钢定级标准不统一的难题,本文采用机器学习对废钢图像进行识别比较,判定废钢等级。构建废钢分类数据集,借助Siamese孪生神经网络对废钢数据集进行训练,选出最优权重,使模型能够准确区分不同种类的废钢。通过使用Siamese网络计算方法比较废钢基准与待测图像的相似度。根据相似度结果判断废钢等级,当相似度趋近于1时,认为废钢形状相似;当相似度趋近于0时,认为废钢形状不同,以此判定废钢图像中废钢种类分布状况。实测结果表明,利用相似比较法和Siamese神经网络进行废钢分类的方法展现出了较好的准确性和可靠性。与传统的人工分类方法相比,这种方法不仅大幅提高了分类效率,且实现了废钢定级的标准化和一致性。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 constructed,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 approaches 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 similarity comparison and the Siamese neural network exhibits excellent accuracy and reliability.Compared with traditional manual classification methods,this approach not only significantly improves classification efficiency but also achieves standardization and consistency in scrap steel grading.

关 键 词：废钢回收 孪生网络 图像匹配 相似度 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] TF702.3[冶金工程—钢铁冶金]