Summary of How to Design a Dataset Compliant with An Ml-based System Odd?, by Cyril Cappi et al.
How to design a dataset compliant with an ML-based system ODD?
by Cyril Cappi, Noémie Cohen, Mélanie Ducoffe, Christophe Gabreau, Laurent Gardes, Adrien Gauffriau, Jean-Brice Ginestet, Franck Mamalet, Vincent Mussot, Claire Pagetti, David Vigouroux
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 research presents a novel approach to designing and validating datasets for Machine-Learning (ML) systems, focusing on Vision-based Landing tasks. The Operational Design Domain (ODD) concept is translated into actionable image-level properties, enabling the definition of verifiable Data Quality Requirements (DQRs). The Landing Approach Runway Detection (LARD) dataset, combining synthetic imagery and real footage, serves as a case study to illustrate this framework’s effectiveness in ensuring ML-based system certification for safety-critical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating special rules for Machine Learning systems that are used in important situations like landing planes safely. The goal is to make sure the data used by these systems is good quality and meets certain requirements. To do this, they developed a framework that takes into account what the system needs to know and translates it into specific image features. They also created a dataset with real and fake images to test this approach. |
Keywords
* Artificial intelligence * Machine learning
