Traditional object recognition algorithms cannot meet the requirements of object recognition accuracy in the actual warehousing and logistics field. In recent years, the rapid development of the deep learning theory has provided a technical approach for solving the above problems, and a number of object recognition algorithms has been proposed based on deep learning, which have been promoted and applied. However, deep learning has the following problems in the application process of object recognition: First, the nonlinear modeling ability of the activation function in the deep learning model is poor; second, the deep learning model has a large number of repeated pooling operations during which information is lost. In view of these shortcomings, this paper proposes multiple-parameter exponential linear units with uniform and learnable parameter forms and introduces two learned parameters in the exponential linear unit (ELU), enabling it to represent piecewise linear and exponential nonlinear functions. Therefore, the ELU has good nonlinear modeling capabilities. At the same time, to improve the problem of losing information in the large number of repeated pooling operations, this paper proposes a new global convolutional neural network structure. This network structure makes full use of the local and global information of different layer feature maps in the network. It can reduce the problem of losing feature information in the large number of pooling operations. Based on the above ideas, this paper suggests an object recognition algorithm based on the optimized nonlinear activation function-global convolutional neural network. Experiments were carried out on the CIFAR100 dataset and the ImageNet dataset using the object recognition algorithm proposed in this paper. The results show that the object recognition method suggested in this paper not only has a better recognition accuracy than traditional machine learning and other deep learning models but also has a good stability and robustness.

传统的对象识别算法不能满足实际仓储物流领域中对象识别精度的要求。近年来，深度学习理论的飞速发展为解决上述问题提供了一种技术途径，并且基于深度学习提出了许多目标识别算法，并得到了推广和应用。然而，深度学习在对象识别的应用过程中存在以下问题：首先，深度学习模型中激活函数的非线性建模能力较差；其次，深度学习模型具有大量重复的池化操作，在此期间信息会丢失。鉴于这些缺点，本文提出了具有统一且可学习的参数形式的多参数指数线性单元，并在指数线性单元（ELU）中引入了两个学习的参数，从而使其能够代表分段线性和指数非线性函数。因此，ELU具有良好的非线性建模能力。同时，为解决大量重复池化操作中信息丢失的问题，提出了一种新的全局卷积神经网络结构。这种网络结构充分利用了网络中不同层特征图的本地和全局信息。它可以减少大量池化操作中丢失特征信息的问题。基于以上想法，本文提出了一种基于优化的非线性激活函数-全局卷积神经网络的目标识别算法。使用本文提出的目标识别算法对CIFAR100数据集和ImageNet数据集进行了实验。结果表明，本文提出的目标识别方法不仅具有比传统机器学习和其他深度学习模型更高的识别精度，而且具有良好的稳定性和鲁棒性。