基于改进残差网络的空纱筒识别模型

Model for empty bobbin recognition based on improved residual network

针对纺织车间背景复杂、纱筒种类多导致利用传统机器视觉识别空纱筒准确率低、模型参数量大的问题,设计了一种基于改进残差网络的空纱筒识别模型。该模型借鉴ResNet系列的模型结构,进行卷积核轻量化,改进经典的残差模块并加入SENet注意力机制,以达到提高检测空纱筒的准确率,减少模型参数的目的。最后通过数据增强,创建了适合工厂实际生产的纱筒数据集。实验结果表明:在消融实验中,应用SENet注意力机制可以提高3.86%的准确率,利用优化残差模块不仅减少了650%的模型参数还提高了1.22%的准确率。在原数据集的验证集上,改进模型的准确率为99.6%比ResNet-18模型高4.46%,与VGG-16和AlexNet相比提高了7.05%~9.41%。在增强的数据集上,识别模型的准确率都有了较大的提升,但改进模型的准确率变化不大,说明该模型的鲁棒性较好,不易受到样本不足的影响。改进模型的参数数量缩小到原模型参数数量的1/10左右,为嵌入式设备部署空纱筒识别模型提供了思路。

Objective In the automatic production line of circular weft knitting robot,the use of conventional machine vision to identify empty yarn cylinders has low accuracy and a large number of model parameters in the process of automatic empty cylinder changing of the bobbin changing robot,due to the complex background of the textile workshop and the many types of yarn cylinders.In order to ensure the accurate identification of empty bobbin by bobbin changing robots,it is necessary to design an empty bobbin identification model with high accuracy and light weight.Method Based on the ResNet-18 model,the convolution kernel was light weighted,the classical residual module was improved,the SENet attention mechanism was increased,and the detection accuracy of empty bobbin was improved.By simulating various interference factors at the production site,the training samples were increased,aiming to improve the robustness of the model and to make it more suitable for the actual production environment.The model before and after improvement was compared with other detection models.Results The original dataset was adopted to study the influence of convolutional kernel lightweight design,attention mechanism and improved residual module on the model.Ablation experimentsshowed that the application of small convolution helped reduce the model parameters to a certain extent,the addition of attention mechanism improved the recognition accuracy by 3.86%,and the addition of the optimized residual structure not only improved the recognition accuracy by 1.22%,but also reduced the amount of model parameters by 650%.Under the same experimental conditions,the detection resultswere compared among the improved model,ResNe-18,VGG-16,and AlexNet network model.The accuracy of the improved model in the verification set is 99.6%,which is 4.46%higher than that of the ResNet-18 model,and 7.05%-9.41%higher than that of VGG-16 and AlexNet.Under the experimental conditions of the same training parameters and network structure,the training on the data-enhanced dataset was verified.Because the data enhancement could improve the diversification of the spindle data and effectively avoid the phenomenon of overfitting,the accuracy of the original model and the improved model were improved.The accuracy of the improved model was 0.43%-0.72%higher than that of the ResNet-18 model,and the accuracy of the improved model was less affected by the dataset,indicating better robustness of the model against interference from the surrounding conditins.The convergence speed of the improved model was higher than that of other recognition models,the ascent speed was smoother,and the training accuracy was the highest.Tthe reliability and effectiveness of the improved model for identifying empty bobbins are illustrated.The improved model was found far superior to the original model in extracting shallow and deep network features,and it could effectively reduce the loss of yarn feature information during convolution kernel and effectively improve the ability to identify yarn.The number of parameters of the improved model was reduced to about 1/10 of the number of parameters of the original model hence reducing the storage space.The work provides an idea for the application of the empty yarn cylinder identification system based on the residual network model to the edge deployment in textile workshops.Conclusion On the basis of ResNet-18 network,the network is modified,combined with the lightweight of convolution kernel,the SENet attention mechanism and the improved residual module.The new model not only improves the result accuracy but also reduces the number of model parameters in identifying empty bobbins in complex environments.Compared with other recognition models,the improved model has better robustness against interference.The improved model has a small number of parameters,which provides an idea for deploying empty bobbin identification models for embedded devices.