原文传递
Covariate-Shift Generative Adversarial Network and Railway Track Image Analysis
| 题名： | Covariate-Shift Generative Adversarial Network and Railway Track Image Analysis |
| 正文语种： | eng |
| 作者： | Ibrahim Balogun;Nii Attoh-Okine |
| 作者单位： | Dept. of Civil and Environmental Engineering Univ. of Delaware Newark DE 19713;Dept. of Civil and Environmental Engineering Univ. of Maryland College Park MD 20742 |
| 关键词： | Track defects; Generative adversarial networks (GAN); Computer vision; Data imbalance; Deep learning; Covariate shift |
| 摘要： | The acceptance of railway systems as a frontier transportation infrastructure can be attributed to their reliability, safety, and support for green technology. With the recent advances in artificial intelligence and machine learning (AI/ML), the maintenance of railroad transportation systems has taken a different direction, especially in the analysis of railroad big data, leading to real-time processing and detection of railway problems. However, using limited track data may result in overfitting, hindering the accurate implementation of robust models. In this paper, the authors consider generative adversarial networks (GANs) with keen consideration for possible covariate shifts to improve track defect detection and decrease data imbalance. The results show that implementing covariate-shift GAN (COGAN) reduces image processing time and eliminates image biases. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 3 |
| 页码： | 04022158.1-04022158.10 |
相关文献
- Automatically Replanning Navigation Route by Using Conditional Generative Adversarial Networks
- Generative Adversarial Network Approach to Future Sermonizing of Housing Dispersal in Emerging Cities
- Traffic Incident Impact Dynamic Prediction with Information Enhancement Cascade Forest
- The Data Science Behind Improving Rail Maintenance and Controlling Costs
- Intelligent Generative Design for Shear Wall Cross-Sectional Size Using Rule-Embedded Generative Adversarial Network
- Predictive Maintenance Model for Ballast Tamping
检索历史
应用推荐
