# Certified ML Object Detection for Surveillance Missions

arXiv: https://arxiv.org/abs/2406.12362 · HTML: https://mlsanity.com/paper/2406.12362/

## 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.

## Builds on (most cited first, 10 of 10)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Mind the Pad -- CNNs can Develop Blind Spots](https://mlsanity.com/paper/2010.02178/) (2020)
- [The 2nd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2108.09909/) (2021)
- [Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms](https://mlsanity.com/paper/2102.02147/) (2021)
- [Zone Evaluation: Revealing Spatial Bias in Object Detection](https://mlsanity.com/paper/2310.13215/) (2023)
- [Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Products](https://mlsanity.com/paper/2307.07681/) (2023)
- [Efficient and Mathematically Robust Operations for Certified Neural Networks Inference](https://mlsanity.com/paper/2401.08225/) (2024)
- [On SORA for High-Risk UAV Operations under New EU Regulations: Perspectives for Automated Approach](https://mlsanity.com/paper/2303.02053/) (2023)

## 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.