Summary of Generative Model-driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection, by Rahatara Ferdousi et al.
Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection
by Rahatara Ferdousi, Chunsheng Yang, M. Anwar Hossain, Fedwa Laamarti, M. Shamim Hossain, Abdulmotaleb El Saddik
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to address the scarcity of large datasets for rail defect detection using Variational Autoencoder (VAE) models. The study focuses on generating synthetic images of rail defects using VAEs with weight decay regularization and image reconstruction loss. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real samples across five classes, achieving remarkable results. A Visual Transformer (ViT) model is fine-tuned using this synthetic CPR dataset, demonstrating high accuracy rates in classifying the five defect classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers better at detecting problems on trains. Right now, it’s hard to train these computer systems because there aren’t enough pictures of different kinds of defects. The researchers tried to solve this problem by using special math that can create new fake pictures of defects that look like the real ones. They tested this idea with a small group of real pictures from the Canadian Pacific Railway and created 500 fake ones. Then, they used these fake pictures to train a computer program that could identify different types of defects really well. |
Keywords
* Artificial intelligence * Regularization * Transformer * Variational autoencoder * Vit
