Spectral Space Transfer Learning and Generalization for Vehicle Recognition

With the surge in the number of vehicles and the urgent need for rapid development of new infrastructure, the demand for efficient vehicle identification in smart transportation and urban management has become increasingly prominent in order to cope with challenges such as traffic flow control, illegal vehicle inspections, and community security inspections. Although deep learning vehicle recognition methods can be very accurate, two main problems happen when using them in real situations. First, getting high-quality labeled data costs a lot of money and takes a lot of time. Second, when the model is used in new places, its performance drops a lot. Transfer learning helps reduce the need for labeled data in the target area. It uses knowledge from models pre-trained in the source area. Then it fine-tunes these models with data from the target area. This makes the model adapt better and learn more efficiently. Generalization means training models to keep good performance in completely new target areas. Its main goal is learning the most important features that don’t depend on specific data patterns. This paper focuses on the data scarcity and cross-domain generalization problems in vehicle recognition, and systematically evaluates the performance of transfer learning and generalization strategies on five mainstream deep neural network models (ResNet, EfficientNet, GoogLeNet, DenseNet, VGG), providing a reference for addressing practical application challenges.