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

Hongbin Zhang¹, Zhijie Li¹, Wengang Wang¹

¹School of Software, East China JiaoTong University, Nanchang, China.

Peerj. Computer Science

|September 14, 2023

Summary

This summary is machine-generated.

This study introduces a new deep learning model for detecting road conditions in all lighting. The multi-supervised bidirectional fusion network (MBFN) accurately identifies dry, wet, and snowy surfaces, enhancing autonomous driving safety.

Keywords:

Automatic driving ConvNeXt Multi-supervised bidirectional fusion network Road surface condition

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Driving Systems

Background:

Ensuring safety in autonomous driving necessitates accurate perception of road surface conditions.
Current road condition recognition models lack robustness and generalization, particularly in varying light conditions.
Most existing research focuses on daylight image analysis, neglecting nighttime and adverse weather scenarios.

Purpose of the Study:

To develop a robust and generalizable model for detecting weather-induced road surface conditions.
To enable reliable autonomous vehicle operation during both day and night.
To address the limitations of existing models in handling diverse road conditions.

Main Methods:

Proposed a novel multi-supervised bidirectional fusion network (MBFN).
Utilized ConvNeXt for feature extraction and a bidirectional fusion module for enhanced feature representation.
Employed a multi-supervised loss function for model training.
Validated the model on two public datasets.

Main Results:

The MBFN model accurately classified diverse road surface conditions (dry, wet, snowy).
Achieved superior performance compared to state-of-the-art baseline models.
Demonstrated competitive performance across various road conditions with model variants.

Conclusions:

The MBFN model offers a robust solution for real-time road surface condition detection.
The proposed approach significantly improves the safety and reliability of autonomous driving systems.
The model's ability to perform in both day and night conditions marks a significant advancement.