RRCNN-EM: A Deep Learning Framework for Hyperspectral Road Surface Segmentation to Enhance Automotive Safety

Abstract

Recent advances in hyperspectral imaging technology have created new opportunities for enhancing
roadway safety through automated surface-condition monitoring. Traditional road inspection techniques,
relying on RGB or infrared imagery, often fail to distinguish visually similar materials such as dry
pavement, oil spills, water films, or surface cracks – conditions that significantly affect vehicle traction
and traffic safety. To address these limitations, this research proposes a recurrent residual convolutional
neural network with ensemble modeling (RRCNN-EM) for hyperspectral road-surface segmentation and
hazard classification. The proposed framework integrates a Weiner filter for denoising hyperspectral data,
followed by RRCNN-based segmentation to preserve spatial dependencies and handle high-dimensional
spectral information.