多尺度特征融合注意力机制的滤棒质检算法

Filter Rod Quality Inspection Algorithm Based on Multi-scale Feature Fusing Attention Mechanism

为解决卷烟滤棒生产过程中需要人工对滤棒指标进行监测并调整设备参数,导致成本高昂且效率低下的问题,提出一种多尺度特征融合注意力机制的滤棒质检算法。该算法利用不同大小的一维卷积获取滤棒多尺度的特征,降低了滤棒局部特征遗漏的可能性。为进一步增强特征表示,本文引入注意力机制使算法模型聚焦于更重要的特征。实验结果表明,与一维卷积神经网络(1D convolutional neural network,1DCNN)、反向传播(back propagation,BP)神经网络、循环神经网络等5种方法相比较,本文提出的算法模型在测试集上的性能更加突出,尤其是与1DCNN、BP神经网络和分布式梯度增强库这3种方法相比,模型的准确率分别提高了3.27%、4.28%和5.70%;精确率分别提高了3.12%、4.68%和5.10%;召回率分别提高了3.28%、4.57%和2.97%;F1-Score分别提高了3.20%、4.13%和4.33%。本文提出的算法不仅可以提高滤棒的生产效率,还可以降低人工成本,具有良好的工程实用价值。

To address the high cost and low efficiency caused by manual monitoring and adjustment of equipment parameters in the production of cigarette filter rods,a filter rod quality inspection algorithm with multi-scale feature fusing attention mechanism is proposed.The algorithm uses one-dimensional convolutions of various sizes to obtain the multi-scale features of the filter rod,which greatly reduces the possibility of missed detection of local features.To further enhance the feature representation,this paper introduces an attention mechanism,enabling the model to focus on regions with richer feature information.Experimental results show that compared with five existing methods such as 1D convolutional neural network(1DCNN),back propagation(BP)neural network and recurrent neural network(RNN),the proposed algorithm shows superior performance on the test set,especially compared with the three methods of 1DCNN,BP neural network and extreme gradient boosting.Specifically,the accuracy of the model increases by 3.27%,4.28%and 5.70%,the precision improves by 3.12%,4.68%and 5.10%,the recall rate rises by 3.28%,4.57%and 2.97%,and the F1-Score enhances by 3.20%,4.13%and 4.33%,respectively.In general,the algorithm proposed in this paper not only increases the production efficiency of filter rods,but also reduces labor costs,demonstrating practical engineering value.