基于深度学习的废钢分类评级方法研究  

Classification and Rating of Scrap Steel Based on Deep Learning

作　　者：肖鹏程 徐文广 常金宝 朱立光[3,4] 朱荣 许云峰[5] XIAO Pengcheng;XU Wenguang;CHANG Jinbao;ZHU Liguang;ZHU Rong;XU Yunfeng(College of Metallurgy and Energy,North China Univ.of Sci.and Technol.Univ.,Tangshan 063210,China;HBIS Group Co.,Ltd.(HBIS),Shijiazhuang,050000,China;Metallurgical and Ecological Eng.School,Univ.of Sci.and Technol.Beijing,Beijing 100083,China;College of Materials Sci.and Eng.,Hebei Univ.of Sci.and Technol.,Shijiazhuang 050000,China;College of Info.Sci.and Eng.,Hebei Univ.of Sci.and Technol.,Shijiazhuang 050000,China)

机构地区：[1]华北理工大学冶金与能源学院,河北唐山063210 [2]河钢集团有限公司,河北石家庄050000 [3]北京科技大学冶金与生态学院,北京100083 [4]河北科技大学材料科学与工程学院,河北石家庄050000 [5]河北科技大学信息科学与工程学院,河北石家庄050000

出　　处：《工程科学与技术》2023年第2期184-193,共10页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(51904107);河北省自然科学基金项目(E2020209005,E2021209094);河北省高等学校科学技术研究项目(BJ2019041);河北省“三三三人才工程”资助项目(A202102002);唐山市人才资助重点项目(A202010004)。

摘　　要：废钢是现代钢铁工业重要的铁素来源,是钢企实现碳中和的重要原料。不同级别的废钢价格悬殊,其质量直接影响钢企的生产成本和产品质量。因此,废钢入炉前的分类和评级问题,受到钢企的普遍重视和高度关注。针对传统人工方法在废钢的分类评级中所出现的效率低、安全性和公正性差等问题,基于深度学习中的卷积注意力机制和加权双向特征融合网络构建废钢分类评级模型。首先,搭建废钢质量查验物理模型,模拟货车卸载废钢的生产作业场景,采用高分辨率视觉传感器采集不同类别的废钢图像。其次,设计了一种结合注意力与特征融合的废钢验质深度学习模型,将卷积注意力模块(convolutional block attention module,CBAM)加入主干网络对采集的废钢图像数据集进行特征提取,聚焦并保留图像的重要特征;使用双向特征金字塔(bidirectional feature pyramid network,BiFPN)平衡多尺度特征信息,进行多尺度特征融合。最后,在模型预测阶段,利用所构建的废钢质量验质模型进行废钢类别和质量判级,验证模型的精确性与检测效率。基于自制废钢验证数据集,与主流的目标检测模型Faster R–CNN、YOLOv4、YOLOv5系列以及YOLOv7进行性能比较。实验结果表明:本研究构建的废钢质量验质模型识别判级的准确率Acc达到了86.8%,所有类别平均精度m AP为89.2%,均高于对比的目标检测模型,在准确性、实时性以及识别评级效率方面可满足实际生产应用,解决废钢分类评级过程中的诸多难题,实现废钢的智能验质和公正判定。Steel scrap is an important source of ferrite for the modern steel industry and an important raw material for steel companies to achieve carbon neutrality. The price of different grades of scrap varies greatly and its quality directly affects the production cost and product quality of steel enterprises. Therefore, the classification and grading of scrap before feeding into the furnace has received widespread attention and great concern from steel enterprises. To address the problems of low efficiency, poor safety, and fairness in the classification and rating of scrap by traditional manual methods, a scrap classification and rating model(CCBFNet) based on the spatial and channel attention mechanism and weighted bidirectional feature fusion network was proposed in the paper. Firstly, a physical model of scrap quality checking was built to simulate the production operation scene of unloading scrap by trucks, and high-resolution vision sensors were used to collect the images of different types of scrap. Secondly, a deep learning model combining attention and feature fusion was designed for scrap quality inspection in the model training stage, and the spatial and channel attention module(CBAM) was added to the backbone network to extract features from the collected scrap image dataset, focusing and retaining the important features of the images;then, a weighted Bidirectional Feature Pyramid Network(BFPN) was used. Secondly, the multi-scale feature fusion was performed by balancing the multi-scale feature information using the Bidirectional Feature Pyramid Network(BiFPN). Finally, in the model prediction stage, the constructed scrap quality verification model CCBFNet was used for scrap category and quality grading to verify the accuracy and detection efficiency of the model. Based on the homemade scrap validation dataset, the performance of CCBFNet was compared with the mainstream target detection Faster R–CNN, YOLOv4, YOLOv5 series, and YOLOv7. The experimental results showed that the Acc of CCBFNet reaches 86.8% a

关 键 词：再生钢铁原料 废钢智能评级 深度学习 注意力机制 

分 类 号：TP274.5[自动化与计算机技术—检测技术与自动化装置]