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
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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2022, Vol. 43 
