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Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset.

Wansik Choi1, Jun Heo2, Changsun Ahn1

  • 1School of Mechanical Engineering, Pusan National University, Busan 46241, Korea.

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|November 27, 2021
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Summary
This summary is machine-generated.

Generating diverse road surface data using CycleGAN improves autonomous driving systems. This method balances datasets, enhancing deep neural network performance for safer vehicle control in various conditions.

Keywords:
CycleGANdeep neural networkroad friction detectionroad surface detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Accurate road surface detection is critical for autonomous vehicle safety.
  • Deep neural networks (DNNs) are widely used for road surface detection.
  • Imbalanced datasets, with a prevalence of dry surfaces, hinder DNN performance.

Purpose of the Study:

  • To address the challenge of imbalanced datasets in road surface detection.
  • To improve the performance of autonomous driving systems through enhanced data.
  • To propose a novel method for augmenting road surface datasets.

Main Methods:

  • Utilizing CycleGAN for artificial image generation.
  • Generating synthetic images of wet and snow-covered roads.
  • Training a road surface detection algorithm on a CycleGAN-augmented dataset.

Main Results:

  • The road surface detection algorithm trained on the augmented dataset showed improved Intersection over Union (IoU).
  • CycleGAN-generated images effectively balanced the dataset.
  • Enhanced DNN performance was observed with the augmented dataset.

Conclusions:

  • CycleGAN-generated images are valuable for training robust road surface detection models.
  • This data augmentation technique improves DNN performance for autonomous driving.
  • The proposed method simplifies the data acquisition process for road surface analysis.