For the logistics sorting warehouse without much light is complex, and the difference between express packaging is not obvious, a fast recognition method of sorting images based on deep learning and dual tree complex wavelet transform was studied. Sorting images are not very clear due to factors such as the enclosed environment and the weak lighting conditions of the warehouse. First, the dual tree complex wavelet transform is used to preprocess the sorting image for noise reduction and other image preprocessing. Second, a convolutional neural network (CNN) was designed. On the basis of Alexnet neural network, parameters of convolutional layer, ReLU layer and pooling layer of CNN are redefined to accelerate the learning speed of neural network. Lastly, according to the new image classification task, the last three layers of the neural network, the full connection layer, the softmax layer and the classification output layer are defined to adapt to the new image recognition. The proposed fast sorting image recognition technology based on deep learning has higher training speed and recognition accuracy in the face of more complicated sorting image recognition, which can meet the experimental requirements. Rapid identification of sorting images is of great significance to improve the efficiency of logistics in unmanned warehouses.

中文翻译：

---

基于深度学习的复杂排序图像快速识别方法研究

针对光线不足的物流分拣仓库复杂，快递包装区别不明显的问题，研究了一种基于深度学习和双树复小波变换的分拣图像快速识别方法。受封闭环境、仓库光线不足等因素影响，分拣图像不是很清晰。首先，采用双树复小波变换对排序后的图像进行预处理，进行降噪等图像预处理。其次，设计了卷积神经网络（CNN）。在Alexnet神经网络的基础上，重新定义了CNN的卷积层、ReLU层和池化层的参数，加快了神经网络的学习速度。最后，根据新的图像分类任务，神经网络的最后三层，定义了全连接层、softmax层和分类输出层以适应新的图像识别。所提出的基于深度学习的快速排序图像识别技术在面对更复杂的排序图像识别时具有更高的训练速度和识别准确率，能够满足实验要求。分拣图像的快速识别对提高无人仓库物流效率具有重要意义。