摘要
为提高小规模训练集下CNN特征驱动的浮选工况识别效果,提出一种基于泡沫红外与可见光图像CNN特征提取及自适应迁移学习的工况识别方法.首先构建基于AlexNet的双模态CNN特征提取及识别模型,并通过RGB-D大规模数据集对模型的结构参数进行预训练;其次,用多个串联的双隐层自编码极限学习机代替预训练模型的全连接层,实现对双模态CNN特征的融合及逐层抽象提取,然后通过核极限学习机映射到更高维空间进行决策;最后构建浮选小规模数据集对迁移后的模型进行训练,并改进量子狼群算法用于模型参数优化.实验结果表明:自适应迁移学习能够明显提高小样本数据集下的识别准确度,采用双模态CNN迁移学习较单模态CNN迁移学习的工况识别精度提高了3.06%,各工况的平均识别准确率达到96.83%,识别精度和稳定性较现有方法有较大提升.
In order to improve the effect of CNN feature driven flotation performance recognition under small-scale training set,a method of flotation performance recognition based on adaptive transfer learning and CNN features extraction of foam infrared and visible images is proposed.Firstly,a dual-modality CNN feature extraction and recognition model based on AlexNet was constructed,and the structural parameters of the model were pre-trained through RGB-D large-scale data set.Secondly,a series of double hidden layer automatic encoder extreme learning machine is used to replace the full connection layer of the pre-training model,so that the dual-modality CNN features can be fused and abstracted layer by layer,and then the decision is made by mapping to higher dimensional space through the kernel extreme learning machine.Finally,the floatation small-scale data set is constructed to train the migrated model,and the improved quantum wolf pack algorithm is used for model parameter optimization.Experimental results show that,adaptive transfer learning can significantly improve the accuracy of recognition in small sample data sets,the accuracy of performance recognition using dual-modality CNN transfer learning is 3.06%higher than that of single-mode CNN transfer learning,and the average recognition accuracy of each working condition reached 96.86%.The accuracy and stability of flotation performance recognition is greatly improved compared with the existing methods.
作者
廖一鹏
杨洁洁
王志刚
王卫星
LIAO Yi-peng;YANG Jie-jie;WANG Zhi-gang;WANG Wei-xing(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China;Fujian Jindong Mining Co.Ltd.,Sanming,Fujian 365101,China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2020年第10期167-178,共12页
Acta Photonica Sinica
基金
国家自然科学基金(Nos.61471124,61601126)
福建省自然科学基金(No.2019J01224)
福建省中青年教师教育科研项目(No.JT180056)。
关键词
机器视觉
浮选工况识别
红外与可见光图像
卷积神经网络
迁移学习
双隐层自编码极限学习机
量子狼群算法
Machine vision
Flotation performance recognition
Infrared and visible images
Convolutional neural network
Transfer learning
Double hidden layer automatic encoder extreme learning machine
Quantum wolf pack algorithm
