Certified ML Object Detection for Surveillance Missions
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Abstract (arXiv)
In this paper, we present a development process of a drone detection system involving a machine learning object detection component. The purpose is to reach acceptable performance objectives and provide sufficient evidences, required by the recommendations (soon to be published) of the ED 324 / ARP 6983 standard, to gain confidence in the dependability of the designed system.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 18 Jun 2024.
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10 of 24 references.
References · 10
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2015Fast R-CNNcited by 2,933
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2020Mind the Pad -- CNNs can Develop Blind Spotscited by 41
- 2021The 2nd Anti-UAV Workshop & Challenge: Methods and Resultscited by 13
- 2021Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platformscited by 7
- 2023Zone Evaluation: Revealing Spatial Bias in Object Detectioncited by 7
- 2023Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Productscited by 4
- 2024Efficient and Mathematically Robust Operations for Certified Neural Networks Inferencecited by 2
- 2023On SORA for High-Risk UAV Operations under New EU Regulations: Perspectives for Automated Approachcited by 1
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