# Runway Sign Classifier: A DAL C Certifiable Machine Learning System

arXiv: https://arxiv.org/abs/2310.06506 · HTML: https://mlsanity.com/paper/2310.06506/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [White Paper Machine Learning in Certified Systems](https://mlsanity.com/paper/2103.10529/) (2021)
- [Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications](https://mlsanity.com/paper/2209.13975/) (2022)
- [AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms](https://mlsanity.com/paper/2302.10292/) (2023)

## Built on by (most cited first, 2 of 2)

- [DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace](https://mlsanity.com/paper/2401.02283/) (2024)
- [On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks](https://mlsanity.com/paper/2608.20053/) (2026)

## Abstract (arXiv)

In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous operation of large commercial airplanes. However, ML technology faces major incompatibilities with existing airborne certification standards, such as ML model traceability and explainability issues or the inadequacy of traditional coverage metrics. Certification of ML-based airborne systems using current standards is problematic due to these challenges. This paper presents a case study of an airborne system utilizing a Deep Neural Network (DNN) for airport sign detection and classification. Building upon our previous work, which demonstrates compliance with Design Assurance Level (DAL) D, we upgrade the system to meet the more stringent requirements of Design Assurance Level C. To achieve DAL C, we employ an established architectural mitigation technique involving two redundant and dissimilar Deep Neural Networks. The application of novel ML-specific data management techniques further enhances this approach. This work is intended to illustrate how the certification challenges of ML-based systems can be addressed for medium criticality airborne applications.