An Enhanced Low-Resolution Image Recognition Method for Traffic Environments

TL;DR

This paper proposes a dual-branch residual network with feature subspace and knowledge distillation to improve low-resolution traffic image recognition accuracy and efficiency.

Contribution

It introduces a novel dual-branch residual network architecture that enhances feature extraction and reduces computational costs for low-resolution traffic images.

Findings

Improved recognition accuracy on low-resolution traffic images.

Reduced network parameters through knowledge distillation.

Effective feature extraction using residual modules and intermediate-layer features.

Abstract

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.