Automated Road Distress Detection Using Vision Transformersand Generative Adversarial Networks

TL;DR

This paper investigates the use of vision transformers and GAN-generated synthetic data for automated road distress detection, demonstrating improved segmentation accuracy over traditional CNN methods.

Contribution

It introduces a novel approach combining GANs and vision transformers for enhanced road distress segmentation, outperforming CNN-based models.

Findings

GAN data improves model performance

MaskFormer outperforms CNN in key metrics

Synthetic data aids training efficiency

Abstract

The American Society of Civil Engineers has graded Americas infrastructure condition as a C, with the road system receiving a dismal D. Roads are vital to regional economic viability, yet their management, maintenance, and repair processes remain inefficient, relying on outdated manual or laser-based inspection methods that are both costly and time-consuming. With the increasing availability of real-time visual data from autonomous vehicles, there is an opportunity to apply computer vision (CV) methods for advanced road monitoring, providing insights to guide infrastructure rehabilitation efforts. This project explores the use of state-of-the-art CV techniques for road distress segmentation. It begins by evaluating synthetic data generated with Generative Adversarial Networks (GANs) to assess its usefulness for model training. The study then applies Convolutional Neural Networks (CNNs) for road distress segmentation and subsequently examines the transformer-based model MaskFormer. Results show that GAN-generated data improves model performance and that MaskFormer outperforms the CNN model in two metrics: mAP50 and IoU.