NAFENet:基于全局注意力特征融合的螺纹扭矩曲线分类网络

NAFENet: Classification Network for Thread Torque Curves Based on Global Attention Feature Fusion

为了提高螺纹油套管气密封检测的工作效率,本文提出了一种基于全局注意力特征融合的螺纹扭矩曲线自动分类网络,即NAFENet.具体来说,NAFENet为了增强模型的表达力,将EfficientNet-B0的卷积结构扩展至11层得到EfficientNet-B11.同时,在其每个MBConv卷积层中构建了基于non-local全局注意力和AFF特征融合模块,以帮助模型获取曲线图像中较为全局的信息,提高特征提取能力.实验结果表明,NAFENet在参数量相较于EfficientNet-B0只有小幅度的增加情况下,曲线识别精度有了较大提升,在自制UBT_Curve数据集上,模型准确率达到92.87%.

To improve the seal detection efficiency of threaded oil casing gas,this study proposes an automatic classification network,NAFENet,for threaded torque curves based on global attention feature fusion.Specifically,NAFENet extends the convolutional structure of EfficientNet-B0 to 11 layers to obtain EfficientNet-B11 and enhance the model expressiveness.Meanwhile,the modules based on non-local global attention and attentional feature fusion(AFF)are built in each MBConv convolutional layer to help the model acquire more global information in the curve images and improve the feature extraction ability.The experimental results show that compared with EfficientNet-B0,the parameter number of NAFENet is slightly increased with improved curve identification accuracy,and the model accuracy reaches 92.87%on the homemade UBT_Curve dataset.