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Wheel Hub Recognition Based on ResNet50 and Transfer Learning |
ZHANG Dian-fan1,YANG Zhen-hao2,CHENG Shu-hong2 |
1. Hebei Key Laboratory of Special Delivery Equipment, Yanshan University,Qinhuangdao, Hebei 066004,China
2. Institute of Electrical Engineering,Yanshan University,Qinhuangdao, Hebei 066004,China |
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Abstract Aiming at the problem of false identification in manual wheel hub sorting, a neural network model based on ResNet50 and transfer learning is adopted to identify the model of automobile wheel hub.The parameters of the pretraining model are migrated to the ResNet50 convolution neural network, the output layer of original network is modified, and the transfer learning model based on ResNet50 is constructed.By comparing the training efficiency and accuracy of AlexNet, VGG11, VGG16 and ResNet50 when different volume convolution layer parameters are not fine-tuned, used fine-tuning and frozen, it is proved that the ResNet50 transfer model can not only shorten training time when the parameters of the seven bottleneck fragments are frozen but also achieve higher accuracy under the same iteration cycle.Under the freezing strategy, the TL-ResNet50 transfer learning model is trained to predict each of the eight hubs, and the average accuracy of each hub is over 99%.
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Received: 31 May 2021
Published: 14 November 2022
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[1]赵玉良, 刘伟军, 刘永贤, 等.汽车轮毂在线识别系统的研究[J].机械设计与制造, 2007, 45(10):164-166.
Zhao Y L, Liu W J, Liu Y X, et al B.Research about a kind of automobile wheel classification system online[J]. Machinery Design & Manufacture, 2007, 45(10):164-166.
[2]程淑红, 管永来, 张典范.基于形状匹配及纹理筛选的汽车轮毂型号识别[J].仪器仪表学报, 2017, 38(9):2299-2306.
Cheng S H, Guan Y L, Zhang D F. Wheel model identification based on shape recognition and texture filtering[J]. Chinese Journal of Scientific Instrument, 2017, 38(9):2299-2306.
[3]Liu M Y, Tuzel O, Veeraraghavan A, et al. Fast directional chamfer matching[C]// Computer Vision & Pattern Recognition, IEEE. San Francisco, USA, 2010.
[4]Nanni L, Paci M, Brahnam S, et al. An ensemble of visual features for Gaussians of local descriptors and non-binary coding for texture descriptors[J]. Expert Systems with Application, 2017, 82(Oct.):27-39.
[5]Tong Z, Gao J, Tong S. A convolutional neural network approach for visual recognition in wheel production lines[J]. International Journal of Advanced Robotic Systems, 2020, 17(3):1-13.
[6]程淑红, 马晓菲, 张仕军, 等. 基于多任务分类的吸烟行为检测[J]. 计量学报, 2020, 41(5):538-543.
Cheng S H, Ma X F, Zhang S J, et al. Smoking Detection Algorithm Based on Multitask Classification[J]. Acta Metrologica Sinica, 2020, 41(5):538-543.
[7]张世辉, 耿勇, 张笑维.基于深度图像利用BP网络实现遮挡边界检测[J].计量学报, 2020, 41(10):1205-1211.
Zhang S H, Geng Y, Zhang X W. Using BP Network for Occlusion Boundary Detection Based on Depth Image[J]. Acta Metrologica Sinica, 2020, 41(10):1205-1211.
[8]Pan S J, Tsang I W, Kwok J T, et al. Domain Adaptation via Transfer Component Analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2):199-210.
[9]Liu W, Li W, Gong W. Ensemble of fine-tuned convolutional neural networks for urine sediment microscopic image classification[J]. IET Computer Vision, 2019, 14(2):18-25.
[10]张菁, 钟绿, 杜岗, 等. 基于迁移学习的胃镜图像识别模型的构建及其在胃癌诊断中的应用[J]. 第二军医大学学报, 2019, 40(5):483-491.
Zhang J, Zhong L, Du G, et al. Construction of gastroscopic image recognition model based on transfer learning and its application in gastric cancer diagnosis[J]. Academic Journal of Second Military Medical University, 2019, 40(5):483-491.
[11]Kim T H, Yu C, Lee S W. Facial expression recognition using feature additive pooling and progressive fine-tuning of CNN[J]. Electronics Letters, 2018, 54(23):1326-1328.
[12]Zhang C, Zhang H, Qiao J, et al. Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6):1389-1401.
[13]Sun C, Ma M, Zhao Z, et al. Deep Transfer Learning Based on Sparse Auto-encoder for Remaining Useful Life Prediction of Tool in Manufacturing[J]. IEEE Transactions on Industrial Informatics, 2018, 15(4):2416-2425.
[14]Pan S J, Qiang Y. A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359.
[15]Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks?[C]// International Conference on Neural Information Processing Systems. MIT Press. Kuching, Malaysia, 2014.
[16]Tzeng E, Hoffman J, Darrell T, et al. Simultaneous Deep Transfer Across Domains and Tasks[C]// 2015 IEEE International Conference on Computer Vision (ICCV), IEEE. Santiago, Chile, 2017.
[17]He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. Las Vegas, USA, 2016. |
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