Multi-supervised bidirectional fusion network for road-surface condition recognition.

ABSTRACT: Rapid developments in automatic driving technology have given rise to new experiences for passengers. Safety is a main priority in automatic driving. A strong familiarity with road-surface conditions during the day and night is essential to ensuring driving safety. Existing models used for recognizing road-surface conditions lack the required robustness and generalization abilities. Most studies only validated the performance of these models on daylight images. To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion mo

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10495952 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature