Comparison of vehicle classification.

This study is dedicated to addressing the trade-off between receptive field size and computational efficiency in low-level vision. Conventional neural networks (CNNs) usually expand the receptive field by adding layers or inflation filtering, which often leads to high computational costs. Although expansion filtering was introduced to reduce the computational burden, the resulting receptive field is only a sparse sampling of the tessellated pattern in the input image due to the grid effect. To better trade-off between the size of the receptive field and the computational efficiency, a new multilevel discrete wavelet CNN model (DWAN) is proposed in this paper. The DWAN introduces a four-level discrete wavelet transform in the convolutional neural network architecture and combines it with Convolutional Block Attention Module (CBAM) to efficiently capture multiscale feature information. By reducing the size of the feature maps in the shrinkage subnetwork, DWAN achieves a wider sensory field coverage while maintaining a smaller computational cost, thus improving the performance and efficiency of visual tasks. In addition, this paper validates the DWAN model in an image classification task targeting fine categories of automobiles. Significant performance gains are observed by training and testing the DWAN architecture that includes CBAM. The DWAN model can identify and accurately classify subtle features and differences in automotive images, resulting in better classification results for the automotive fine-grained category. This validation result further demonstrates the effectiveness and robustness of the DWAN model in vision tasks and lays a solid foundation for its generalization to practical applications.

本研究聚焦于解决低层视觉任务中感受野尺寸与计算效率的权衡难题。传统卷积神经网络（CNNs）通常通过堆叠网络层数或采用扩张滤波来拓展感受野，但该方案往往伴随高昂的计算开销。尽管扩张滤波被提出以降低计算负荷，但受网格效应影响，其生成的感受野仅能对输入图像中的镶嵌模式进行稀疏采样。为实现感受野尺寸与计算效率的更优平衡，本文提出一种新型多级离散小波卷积神经网络模型（DWAN）。该模型在卷积神经网络架构中嵌入四级离散小波变换，并与卷积块注意力模块（CBAM）相结合，以高效捕捉多尺度特征信息。通过在收缩子网络中缩减特征图尺寸，DWAN在保持较低计算成本的同时实现了更广的感受野覆盖，进而提升视觉任务的性能与运行效率。此外，本文针对汽车细分类别的图像分类任务对DWAN模型开展了验证。实验结果显示，搭载CBAM的DWAN架构在训练与测试阶段均实现了显著的性能提升。该模型能够精准识别并分类汽车图像中的细微特征与差异，因此在汽车细粒度分类任务中取得了更优异的分类效果。上述验证结果进一步证实了DWAN模型在视觉任务中的有效性与鲁棒性，为其泛化至实际应用场景打下了坚实基础。