Research on Automatic Cargo Recognition in Smart City Environment

Abstract

Smart city refers to the use of various information technologies to improve the lives of citizens. However, in terms of transportation and sales of goods, traditional methods require a lot of manpower and material resources, and cannot be automatically identified. In order to improve the efficiency and accuracy of product identification, product sorting is automated. It uses the powerful feature learning and expression capabilities of deep convolutional neural networks to automatically learn product features, thereby achieving high-precision image classification. Therefore, this paper first proposes an improved VGG network, combines transfer learning to establish a deep learning recognition model, and finally conducts multiple sets of experiments on the 131-category Fruit-360 dataset. The results show that when the Adam optimizer is used for iterative training for 30 rounds and the batch_size is 64, the accuracy of the algorithm proposed in this paper reaches 94.19% on the training set, 97.91% on the validation set, and 92.2% on the test set top1. The accuracy rate on the test set top5 is as high as 100%. Therefore, the method in this paper can solve the problems caused by traditional methods and provide useful help for smart cities.