December 6, 2025PLoS ONEOpen Access

Deep spatial attention networks for vision-based pavement distress perception in autonomous driving

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Authors

FDFuwen DengShandong Institute of Business and Technology JJJiandong JinCentre National de la Recherche Scientifique

Discussion

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Implication

This method improves road pavement surface detection in autonomous driving, indicating effective semantic segmentation and control strategies in real-time monitoring.

Key Points

Autonomous driving accuracy relies on precise road pavement surface perception, enhancing passenger experience.
The approach utilized a dataset of 2,400 annotated images to optimize semantic segmentation performance.
This semantic segmentation method incorporates deep spatial attention networks for improved road damage detection.
The model shows significant gains in detection precision and efficiency, supporting safe driving strategies.

Cite This Study

Deng et al. (2025) studied this question.

synapsesocial.com/papers/69337d09b3f947a0a125aa63 https://doi.org/10.1371/journal.pone.0335745

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Implication

This method improves road pavement surface detection in autonomous driving, indicating effective semantic segmentation and control strategies in real-time monitoring.