Summary of Self-supervised Learning For Identifying Defects in Sewer Footage, by Daniel Otero and Rafael Mateus
Self-Supervised Learning for Identifying Defects in Sewer Footage
by Daniel Otero, Rafael Mateus
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study develops an innovative application of Self-Supervised Learning (SSL) for automated sewer inspection, addressing the need for cost-effective and scalable solutions. The approach is designed to work without relying on large amounts of labeled data, making it particularly suitable for resource-limited settings. The model achieves competitive results, being at least 5 times smaller than other approaches found in the literature, while maintaining performance with only 10% of the available data when training with a larger architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to revolutionize sewer maintenance by proposing an automated solution that uses Self-Supervised Learning (SSL) for defect detection. The approach is designed to be cost-effective and scalable, making it suitable for resource-limited settings. |
Keywords
» Artificial intelligence » Self supervised
