Back Propagation Optimization of Convolutional Neural Network Based on the left and the right hands Identification

N owadays, medical plastic gloves are sorted into the left and the right hands manually with low efficiency during productive process. In this paper, an automated way is proposed to improve this situation through establishing a convolutional neural network model for image recognition. The back propagation process of learning and training is analyzed in order to optimize the weight by adopting the combination of different activation layers and different loss functions. For the same learning times, there are two evaluation indexes. One is the result of recognition accuracy in the training set, the other is the convergence curve and oscillation amplitude of the loss function. Finally, the adaptability of the combinations is discussed, which plays an important role in improving the recognition accuracy of the left and the right hand.