2024
doi: 10.12720/jait.15.9.1019-1024
Evaluating Image-to-Image Translation Techniques for Simulating Physical Conditions of Traffic Signs
Rung-Ching Chen
,
Ming-Zhong Fan ,
William Eric Manongga ,
Chayanon Sub-r-pa*
- Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
- Manuscript receivedFebruary 15, 2024
- revisedFebruary 29, 2024
- acceptedApril 26, 2024
- publishedSeptember 5, 2024
Abstract
Traffic signs are vital in providing important information to drivers, ensuring their safety, and helping them follow the road rules. Object detection algorithms like You Only Look Once (YOLO) are used in autonomous vehicles to monitor traffic sign information. However, most object detection research focuses on identifying traffic signs rather than their physical condition. One major issue with the existing dataset is the lack of data on damaged traffic signs for training, which could adversely affect the performance of the object detection algorithm. To address this problem, our paper comprehensively reviews the Image-to-Image (I2I) algorithm to modify existing traffic sign images to showcase different physical statuses (normal and damaged). We conduct experiments using state-of-the-art unpaired image-to-image translation techniques, UNet Vision Transformer cycle-consistent Generative Adversarial Network (UVCGAN) v2, and Energy-Guided Stochastic Differential Equations (EGSDE) to translate normal and damaged traffic sign images. Our experimental results are evaluated using Fréchet Inception Distance (FID) and side-by-side image comparison. We analyze and discuss possible and future improvements.
Keywords
- traffic sign detection
- image generative
- Image-to-Image (I2I)
- Generative Adversarial Networks (GANs)
- Cycle Generative Adversarial Network (CycleGAN)
- diffusion model
How to Cite
Rung-Ching Chen, Ming-Zhong Fan, William Eric Manongga, and Chayanon Sub-r-pa, "Evaluating Image-to-Image Translation Techniques for Simulating Physical Conditions of Traffic Signs," Journal of Advances in Information Technology, vol. 15, no. 9, pp. 1019-1024, 2024. https://doi.org/10.12720/jait.15.9.1019-1024
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
