How to design a dataset compliant with an ML-based system ODD?

TL;DR

This paper presents a framework for designing and validating datasets that meet the Operational Design Domain (ODD) requirements for ML-based vision systems, ensuring safety and compliance in critical applications.

Contribution

It introduces a systematic process to translate system constraints into verifiable image-level data quality requirements for dataset design.

Findings

Framework for ODD-compliant dataset design

Verification process for Data Quality Requirements

Application to Landing Approach Runway Detection dataset

Abstract

This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications.