基于卷积神经网络的钢渣砂图像识别及图像变化规律  

Steel Slag Sand Image Recognition and Image Change Rule Based on Convolutional Neural Network

作　　者：滕胜杰 朱琳[1] 李运泽 王新年 晋强[1] TENG Sheng-jie;ZHU Lin;LI Yun-ze;WANG Xin-nian;JIN Qiang(College of Hydraulic and Civil Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Deputy Chief Engineer,Department of Xinjiang Corps Urban Construction Group Company,Urumqi 830052,China)

机构地区：[1]新疆农业大学水利与土木工程学院,乌鲁木齐830052 [2]新疆兵团城建集团有限公司,乌鲁木齐830052

出　　处：《科学技术与工程》2024年第1期300-307,共8页Science Technology and Engineering

基　　金：新疆水利工程安全与水灾防治重点实验室项目(ZDSYS-YJS-2021-11);2021年第十一师财政科技计划。

摘　　要：钢渣安定性检验是实现钢渣安全资源利用的关键,针对钢渣安定性检测方法的效率低且受到取样代表性不足的问题,提出一种基于卷积神经网络的钢渣砂图像分类模型SE-ConvNeXt。该分类模型针对钢渣砂的图像特征,在ConvNeXt网络中添加通道注意力机制SE-Net(squeeze and excitation network)。相比于原ConvNeXt和其他卷积神经网络模型,SE-ConvNeXt的收敛速度更快,训练过程更稳定,准确率更高。分别使用2.36~4.75 mm和1.18~2.36 mm两个粒径的钢渣砂图像训练网络,并结合粉化率的变化规律分析。结果表明:模型预测两个粒径的钢渣砂图像数据集准确率分别为92.5%、94%,且钢渣砂图像随着蒸汽陈化时间的增加,变化程度逐渐变小,随后图像变化程度趋于稳定,与粉化率变化规律相似。可见蒸汽处理的钢渣砂可通过钢渣砂图像评价体积安定性。Steel slag stability testing is the key to achieve safe resource utilization of steel slag.To address the issues of low efficiency and inadequate representativeness in stability testing methods,a steel slag image classification model called SE-ConvNeXt based on convolutional neural networks was proposed.The classification model adds channel attention mechanism squeeze and excitation network(SE-Net)to the ConvNeXt network for the image features of steel slag sand.Compared to the original ConvNeXt and other convolutional neural network models,SE-ConvNeXt achieves faster convergence,more stable training,and higher accuracy.The networks were trained using steel slag sand images of two grain sizes,2.36~4.75 mm and 1.18~2.36 mm,respectively,and analyzed in conjunction with the change law of pulverization rate.The results show that the model predicts the accuracy of the steel slag sand image dataset of two particle sizes 92.5% and 94%,respectively,and the steel slag sand image changes gradually with the increase of steam aging time,and then the degree of image change tends to stabilize,similar to the law of change of pulverization rate.It can be seen that steam-treated steel slag sand can be evaluated by the volume stability of steel slag sand images.

关 键 词：图像识别 钢渣砂 ConvNeXt 注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]