摘要
为解决混凝土砂石骨料复杂种类识别精度低的问题,实现砂石骨料种类自动识别,提出了一种适用于混凝土砂石骨料种类识别的CM-ResNet18网络模型。首先采集骨料图像数据集,并采用数据增强方法增加样本以提升模型的鲁棒性;其次选择ResNet18模型作为主干网络,融合CBAM模块和MHSA模块,以增强模型的特征提取能力;最后增加Dropout函数以提高神经网络的泛化性能,同时在训练中引入迁移学习以加快网络收敛速度,并增大最后一层学习率,使其更好地适应训练数据并提高模型性能。实验结果表明,CM-ResNet18模型在原材料识别中取得了高达99.09%的准确率。与其他网络模型AlexNet,VGG19,EfficientNet,ResNet18,ResNet34相比,CM-ResNet18模型在识别准确率、精确率、召回率、F1-score上均有提高,表明该方法在混凝土砂石骨料识别中具有较高的实用性和可行性。
In order to solve the problem of low accuracy of identification of complex types of concrete sand and gravel aggregates and realize the automatic identification of sand and gravel aggregate types,a CM-ResNet18 network model suitable for the identification of concrete sand and gravel aggregate types is proposed.Secondly,the ResNet18 model is selected as the backbone network,the CBAM module and the MHSA module are fused to enhance the model’s ability to extract features,and then the Dropout function is added to improve the generalization performance of the neural network,and transfer learning is introduced into the training to accelerate the network convergence speed,and the last layer learning rate is increased to better adapt to the training data and improve the model performance.Experimental results show that the CM-ResNet18 model achieves an accuracy of up to 99.09%in the identification of raw materials.Compared with other network models AlexNet,VGG19,EfficientNet,ResNet18 and ResNet34,the CM-ResNet18 model has improved the recognition accuracy,precision,recall rate and F1-score,and the results show that the method has high practicability and feasibility in the identification of concrete sand and gravel aggregates.
作者
曹庆园
朱建鸿
CAO Qingyuan;ZHU Jianhong(Key Laboratory of Advanced Process Control for Light Industry,Jiangnan University,Wuxi,Jiangsu 214122,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S02期298-303,共6页
Computer Science
基金
国家自然科学基金(61973139)。
关键词
砂石骨料
数据增强
残差网络
注意力机制
迁移学习
Sand and gravel aggregate
Data augmentation
Residual network
Attention mechanisms
Transfer learning
作者简介
CAO Qingyuan,born in 1998,master’s candidate.His main research interests include machine vision and computer intelligent control system research,1249015804@qq.com;通信作者:ZHU Jianhong,born in 1964,associate professor,master tutor.His main research interests include Internet of Things and industrial automation intelligent control system.zhu0012008@163.com。
