End-to-end dehazing of traffic sign images using reformulated atmospheric scattering model

Abstract

As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images.

MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images

“…These algorithms learn the mapping relationship between a large number of haze images and corresponding clear images through deep learning networks to achieve efficient haze removal effects. Dehazing algorithms based on deep learning models can be divided into two types: the first is based on atmospheric scattering model dehazing [12], and the second is based on end-to-end model dehazing [13].…”