# Certified ML Object Detection for Surveillance Missions

Source: [Certified ML Object Detection for Surveillance Missions](<https://www.sota2.com/research/paper/68fa73aa1e209131cea5dca4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:54:13.197Z

Authors: Mohammed Belcaid, Eric Bonnafous, Louis Crison, Christophe Faure, Eric Jenn, Claire Pagetti

Published: 2024-06-18T07:42:22.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.12362>)

[PDF](<https://arxiv.org/pdf/2406.12362.pdf>)