  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.
