关键词: 细粒度图像分类；车辆识别；特征融合；特征金字塔

Abstract: In order to solve the problem of large intra-class differences and high inter-class similarity in fine-grained image classification tasks, this paper proposes a multi-scale feature fusion network, which aims to learn and refine more discriminant features from the potentially different features. Among them, the feature pyramid is used as the benchmark network to obtain the difference level semantic features, and the multi-level information can provide a more comprehensive decisionmaking basis for fine-grained image classification tasks. The attention mechanism combines spatial information and channel information to enrich the convolution features. Experimental verification shows that compared with other advanced models, the proposed method effectively improves the classification accuracy, and the recognition accuracy of the vehicle dataset is 93.5%.

Key words: fine-grained image classification; vehicle identification; feature fusion; feature pyramid