Research on rapid recognition of complex sorting images based on deep learning

Abstract

Image recognition technology with faster training speed and higher recognition accuracy has always been the focus and frontier of intelligent technology research. Sorting image fast recognition is of great significance to improve logistics efficiency in unmanned warehouse and other occasions. The simulation of sorting image fast recognition based on deep learning is studied. A convolution neural network is designed. For the specific environment of logistics warehouse and the specified objects to be identified, the sorting image is not very clear because of the closed environment and illumination conditions of warehouse. Firstly, the dual tree complex wavelet transform is used to denoise the sorting image. Then, on the basis of AlexNet neural network, the convolution layer of convolution neural network is dealt with. ReLU layer and pooling layer parameters are redefined to speed up the learning speed of the neural network. Then, according to the new image classification task, the last three layers of the neural network are defined, which are full connection layer, Softmax layer and classification output layer, to adapt to the new image recognition. The proposed fast sorting image recognition technology based on depth learning has higher training speed and recognition accuracy in the face of more complex sorting image recognition.

Keywords

• deep learning
• convolution neural network
• image recognition
• sorting