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

Abstract

Ensuring the safety and comfort of autonomous driving relies heavily on accurately perceiving the quality of the road pavement surface. However, current research has primarily focused on perceiving traffic participants such as surrounding vehicles and pedestrians, with relatively limited investigation into road surface quality perception. This paper addresses this gap by proposing a high-performance semantic segmentation method that utilizes real-time road images captured by an onboard camera to monitor the category and position of road defects ahead of the ego vehicle. Our approach introduces a novel multi-scale spatial attention module to enhance the accuracy of detecting road surface damage within the traditional semantic segmentation framework. To evaluate the proposed approach, we curated and utilized a dataset comprising 2,400 annotated images for model training and validating. Experimental results demonstrate that our method achieves a superior balance between detection precision and computational efficiency, outperforming existing semantic segmentation models in terms of mean IoU while maintaining low computational cost and high inference speed. This approach holds great potential for application in vision-based autonomous driving as it can be seamlessly integrated with appropriate control strategies, thereby offering passengers a smooth and reliable driving experience.