Research on traffic sign image enhancement model under severe weather conditions

Abstract

With the development of intelligent transportation and intelligent connected vehicles, the accuracy of traffic sign recognition directly affects intelligent driving decision-making and driving safety. Nevertheless, traffic sign recognition suffers from issues such as blurred images, reduced contrast and difficult feature extraction under severe weather including rainy, foggy and nighttime conditions. To tackle the above problems, this paper proposes a joint model integrating image enhancement and recognition. A coding-attention-decoding structured image enhancement module is adopted to strengthen the edges of traffic signs against image quality degradation caused by severe weather. Meanwhile, a loss optimization model based on YOLO is established to reduce the missed detection rate and false detection rate of target recognition. Finally, severe weather data are simulated based on public datasets, and experiments verify the effectiveness of the proposed joint model, which provides effective support for the subsequent technological development of intelligent vehicles.