Currently, low-resolution image recognition is confronted with a significant challenge in the field of intelligent traffic perception. Compared to high-resolution images, low-resolution images suffer from small size, low quality, and lack of detail, leading to a notable decrease in the accuracy of traditional neural network recognition algorithms. The key to low-resolution image recognition lies in effective feature extraction. Therefore, this paper delves into the fundamental dimensions of residual modules and their impact on feature extraction and computational efficiency. Based on experiments, we introduce a dual-branch residual network structure that leverages the basic architecture of residual networks and a common feature subspace algorithm. Additionally, it incorporates the utilization of intermediate-layer features to enhance the accuracy of low-resolution image recognition. Furthermore, we employ knowledge distillation to reduce network parameters and computational overhead. Experimental results validate the effectiveness of this algorithm for low-resolution image recognition in traffic environments.

翻译：当前，低分辨率图像识别在智能交通感知领域面临重大挑战。与高分辨率图像相比，低分辨率图像存在尺寸小、质量低、细节缺失等问题，导致传统神经网络识别算法的准确率显著下降。低分辨率图像识别的关键在于有效的特征提取。为此，本文深入研究了残差模块的基本维度及其对特征提取与计算效率的影响。基于实验结果，我们提出了一种双分支残差网络结构，该结构利用残差网络的基本架构和公共特征子空间算法，同时融合中间层特征的使用来提升低分辨率图像识别的精度。此外，我们还采用知识蒸馏技术以减少网络参数量与计算开销。实验结果验证了该算法在交通环境下进行低分辨率图像识别的有效性。