Fine-grained Vehicle Classification Based on Feature Augmentation

Abstract

Fine-grained vehicle classification is a hot research topic in the field of computer vision, which aims to recognize different models of vehicles belonging to the same brand. Limited by the large appearance differences of the same vehicle (due to different attitudes and perspectives) and small appearance differences between different vehicles, the vehicle’s reorganization performance is not high and can’t meet the real application requirements. In this paper, a Fine-grained Vehicle Classification is proposed based on Feature Augmentation (FAFCC), which focuses on mining the key discriminative parts of the object. Specifically, the FAFCC first improves the weakly supervised data augmentation network (WSDAN) by using multi-layer feature extraction and fusion instead of single-layer features. Further, the FAFCC assigns local feature weights to select key features, which can solve the problem that the discriminant features in the channel are not prominent enough and realize the high-quality compact fine-grained vehicle recognition task. The performance of the FAFCC on the fine-grained image classification tasks for vehicles has been evaluated through algorithm comparison experiments and ablation experiments. Through visual analysis, it can be seen that the introduction of each new module has optimized the loss function value and convergence speed of the model. In general, our FAFCC algorithm has a certain improvement effect compared with the original algorithm.